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["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","simulated[seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",0,0.0027059389976784587,2.803436517715454,2.8499248027801514,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","simulated[seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",10,0.12671579292509705,2.8130781650543213,2.8377323150634766,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","simulated[seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",20,0.2417263878742233,2.866976737976074,2.876339912414551,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","simulated[seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",30,0.3566940729506314,2.8544604778289795,2.8559982776641846,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","simulated[seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",0,0.001455835998058319,2.813697338104248,2.846217155456543,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","simulated[seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",10,0.1204521160107106,2.814699649810791,2.84167742729187,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","simulated[seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",20,0.235071687027812,2.8278799057006836,2.881701946258545,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","simulated[seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",30,0.3493480880279094,2.8687727451324463,2.84975004196167,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","simulated[seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",0,0.0011719389585778117,2.8117692470550537,2.8487470149993896,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","simulated[seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",10,0.1170430778292939,2.8135602474212646,2.8528382778167725,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","simulated[seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",20,0.23427542566787452,2.863029718399048,2.8701953887939453,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","simulated[seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",30,0.34911165956873447,2.820082902908325,2.8390281200408936,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","simulated[seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",0,0.001611714018508792,2.799710750579834,2.8423287868499756,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","simulated[seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",10,0.11962198198307306,2.814173698425293,2.8401732444763184,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","simulated[seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",20,0.23484131321310997,2.8554649353027344,2.8870019912719727,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","simulated[seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",30,0.3503142144763842,2.832197427749634,2.851226329803467,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","simulated[seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",0,0.0011770589044317603,2.8259124755859375,2.8456499576568604,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","simulated[seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",10,0.11756200972013175,2.809192180633545,2.8368406295776367,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","simulated[seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",20,0.23204515897668898,2.841318130493164,2.8651540279388428,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","simulated[seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",30,0.35063456930220127,2.839029312133789,2.8516736030578613,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","simulated[seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",40,0.4656282513169572,2.8261961936950684,2.8378584384918213,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","simulated[seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",0,0.0013131509767845273,2.8138201236724854,2.8511338233947754,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","simulated[seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",10,0.12010861188173294,2.8207998275756836,2.845921516418457,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","simulated[seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",20,0.2334850198822096,2.8828625679016113,2.8655877113342285,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","simulated[seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",30,0.347338815103285,2.8351616859436035,2.8367960453033447,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","simulated[seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",0,0.0012066940544173121,2.8085250854492188,2.8408963680267334,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","simulated[seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",10,0.11768044694326818,2.8236913681030273,2.8422176837921143,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","simulated[seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",20,0.23112569167278707,2.866140604019165,2.869678497314453,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","simulated[seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",30,0.3455835726344958,2.856693983078003,2.8443386554718018,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","simulated[seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",0,0.0012025859905406833,2.811678409576416,2.8422605991363525,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","simulated[seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",10,0.11725896806456149,2.804777145385742,2.837132692337036,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","simulated[seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",20,0.23297530598938465,2.8447659015655518,2.8616793155670166,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","simulated[seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",30,0.3486128429649398,2.8109261989593506,2.8381378650665283,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","simulated[seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",40,0.4638081998564303,2.818678379058838,2.838660955429077,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","simulated[seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",0,0.0018854399677366018,2.8015074729919434,2.8383052349090576,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","simulated[seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",10,0.11893793579656631,2.810096502304077,2.8423380851745605,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","simulated[seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",20,0.23336971970275044,2.8549249172210693,2.8654720783233643,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","simulated[seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",30,0.3485713667469099,2.87902569770813,2.8676388263702393,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","simulated[seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",0,0.0012126050423830748,2.8095784187316895,2.8403313159942627,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","simulated[seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",10,0.11803868121933192,2.8142242431640625,2.837723731994629,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","simulated[seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",20,0.2331130332313478,2.844348430633545,2.8488681316375732,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","simulated[seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",30,0.3501613592961803,2.8261444568634033,2.8311915397644043,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",0,0.0036858830135315657,2.8082337379455566,2.843109369277954,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",10,0.17917511076666415,2.806633710861206,2.840003728866577,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",20,0.3462403817102313,2.845522880554199,2.877876043319702,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",30,0.5142145666759461,2.832374095916748,2.848320484161377,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",40,0.6852121816482395,2.8566207885742188,2.866973638534546,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",0,0.0014953879872336984,2.8102681636810303,2.841538667678833,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",10,0.170767264906317,2.8157801628112793,2.843081474304199,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",20,0.33787129959091544,2.835291862487793,2.862112283706665,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",30,0.5078461256343871,2.818608283996582,2.8573520183563232,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",0,0.0017214870313182473,2.8083343505859375,2.846801519393921,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",10,0.16902979812584817,2.811880588531494,2.8446648120880127,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",20,0.33665628905873746,2.828828811645508,2.8493409156799316,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",30,0.5035813411232084,2.844048500061035,2.8553335666656494,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",0,0.0014618670102208853,2.832639694213867,2.8590688705444336,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",10,0.1669920568820089,2.8230814933776855,2.853605031967163,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",20,0.3346341139404103,2.8332293033599854,2.8718509674072266,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",30,0.5014825129183009,2.8094046115875244,2.8522939682006836,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",40,0.6683694338425994,2.8232014179229736,2.8471570014953613,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",50,0.8348803379340097,2.801586627960205,2.8380141258239746,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",60,1.0016724738525227,2.8049259185791016,2.8373234272003174,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",70,1.1678998819552362,2.8111133575439453,2.840574264526367,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",80,1.3349664440611377,2.816229820251465,2.8425469398498535,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",0,0.0015298320213332772,2.8075757026672363,2.841707229614258,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",10,0.17102620482910424,2.815045118331909,2.8426339626312256,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",20,0.33700350078288466,2.864717721939087,2.856713056564331,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",30,0.49992488080170006,2.8438401222229004,2.8472418785095215,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",0,0.0014586600009351969,2.806588649749756,2.845515489578247,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",10,0.16911376419011503,2.8305253982543945,2.856099843978882,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",20,0.3357367580756545,2.8448920249938965,2.8602073192596436,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",30,0.5023284232011065,2.8719022274017334,2.8704066276550293,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",0,0.0014694399433210492,2.807504177093506,2.8435628414154053,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",10,0.16880587197374552,2.8463034629821777,2.866769552230835,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",20,0.33713719504885375,2.8450677394866943,2.8802568912506104,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",30,0.5046379041159526,2.8444528579711914,2.871333122253418,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",0,0.0015315549680963159,2.805055618286133,2.842008590698242,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",10,0.16827042400836945,2.8236734867095947,2.848206043243408,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",20,0.33357852313201874,2.828225612640381,2.848304510116577,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",30,0.4990420389221981,2.8365702629089355,2.8451807498931885,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",0,0.002066515968181193,2.82789945602417,2.866063356399536,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",10,0.16952418209984899,2.8318982124328613,2.862884044647217,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",20,0.3385169499088079,2.8469531536102295,2.863861560821533,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",30,0.504635447752662,2.816978693008423,2.8536994457244873,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",40,0.6714696187991649,2.83732008934021,2.8580162525177,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",50,0.8394607448717579,2.8186392784118652,2.848278760910034,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",60,1.0073409751057625,2.807565689086914,2.8394598960876465,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",70,1.1722718400415033,2.8094263076782227,2.8367042541503906,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",80,1.3382918671704829,2.8256475925445557,2.8440654277801514,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",90,1.505152277299203,2.819171190261841,2.8416566848754883,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",0,0.0014189280336722732,2.804564952850342,2.8378255367279053,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",10,0.16870951687451452,2.8407273292541504,2.852848529815674,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",20,0.337116272887215,2.873417377471924,2.8935012817382812,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",30,0.5039588598301634,2.829805850982666,2.8426177501678467,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.015612352057360113,2.8940184116363525,2.8680765628814697,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.1407067341497168,2.8547635078430176,2.8179941177368164,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,0.26265284908004105,2.8493001461029053,2.8019256591796875,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,0.3903644570382312,2.8459126949310303,2.7857842445373535,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,0.5108970948494971,2.8479549884796143,2.7696280479431152,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,0.6314949920633808,2.849168539047241,2.7540512084960938,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,0.752733136061579,2.859135150909424,2.7381951808929443,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.002102862927131355,2.902391195297241,2.869068145751953,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.1248616308439523,2.8608272075653076,2.8172237873077393,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,0.24561579816509038,2.85538649559021,2.8014824390411377,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,0.36619437008630484,2.8552420139312744,2.7849230766296387,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,0.4862925132038072,2.859860420227051,2.7682549953460693,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,0.6068118921248242,2.8662469387054443,2.7516231536865234,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,0.7274171130266041,2.879786252975464,2.7352771759033203,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.0019032430136576295,2.8675851821899414,2.8533079624176025,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.12359642807859927,2.85115909576416,2.81417179107666,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,0.24403642234392464,2.848315477371216,2.8000922203063965,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,0.36427748017013073,2.8498802185058594,2.784987449645996,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,0.48402385122608393,2.8532848358154297,2.77044939994812,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,0.6041640533367172,2.8591368198394775,2.7556495666503906,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.0020722460467368364,2.9227254390716553,2.9304161071777344,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.12476700532715768,2.8519463539123535,2.8159947395324707,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,0.24487963411957026,2.8478565216064453,2.801722526550293,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,0.3647343550110236,2.847839117050171,2.7870523929595947,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,0.484587874263525,2.8494338989257812,2.7719173431396484,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,0.6073519022902474,2.850816488265991,2.7569401264190674,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,0.7272691180696711,2.858065366744995,2.7411046028137207,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.002087865024805069,2.871502161026001,2.8583903312683105,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.12299186980817467,2.8501265048980713,2.814580202102661,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,0.24319364468101412,2.847656726837158,2.8006491661071777,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,0.36335388163570315,2.843498706817627,2.7867884635925293,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,0.48337520053610206,2.8477089405059814,2.771378993988037,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,0.6032866971800104,2.85290789604187,2.7556848526000977,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,0.7230180603219196,2.8605151176452637,2.74021053314209,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.002133709960617125,2.902682065963745,2.869351387023926,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.1232153158634901,2.858147144317627,2.8194823265075684,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,0.24375602893996984,2.854879856109619,2.806256055831909,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,0.36443893623072654,2.8530633449554443,2.791588306427002,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,0.484222837141715,2.8579347133636475,2.7754178047180176,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,0.6044160261517391,2.868654727935791,2.760687828063965,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,0.72457803809084,2.876965045928955,2.7452049255371094,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.0019801759626716375,2.904493808746338,2.8732199668884277,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.12448688875883818,2.8519208431243896,2.818873167037964,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,0.24430791893973947,2.851893186569214,2.8029422760009766,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,0.3635534418281168,2.8534862995147705,2.787205696105957,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,0.48305856762453914,2.856055736541748,2.771667003631592,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,0.6028501328546554,2.86264705657959,2.7564218044281006,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.0019405219936743379,2.9238553047180176,2.8813116550445557,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.12336752703413367,2.8543646335601807,2.8177833557128906,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,0.24360375618562102,2.8497543334960938,2.8040950298309326,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,0.3635999590624124,2.8479862213134766,2.7883524894714355,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,0.4856594030279666,2.8507559299468994,2.771073579788208,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,0.6052529409062117,2.858802080154419,2.7548723220825195,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,0.7248743429081514,2.8684816360473633,2.73897385597229,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.002111367997713387,2.8634724617004395,2.8593244552612305,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.12373345287051052,2.8500096797943115,2.813854455947876,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,0.2440538788214326,2.8474130630493164,2.798698902130127,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,0.36450475500896573,2.845717191696167,2.7848846912384033,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,0.48457125888671726,2.8474934101104736,2.7712018489837646,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,0.6042943588690832,2.846712112426758,2.7564821243286133,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,0.7240646537393332,2.857273578643799,2.7423973083496094,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.002025339985266328,2.8662593364715576,2.85233736038208,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.12285569892264903,2.857534646987915,2.812328338623047,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,0.24251655279658735,2.852715015411377,2.798351287841797,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,0.3620880910893902,2.8549914360046387,2.784297466278076,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,0.48192503210157156,2.8564352989196777,2.7699270248413086,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,0.6018329511862248,2.858055353164673,2.755936622619629,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.005560462945140898,2.8854563236236572,2.9717459678649902,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.30728812888264656,2.762012481689453,2.7971010208129883,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,0.6137011076789349,2.7619423866271973,2.775858163833618,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,0.9123230348341167,2.7625391483306885,2.7540531158447266,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,1.209752596099861,2.7842466831207275,2.732801914215088,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,1.505970913101919,2.7826786041259766,2.70550799369812,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.0038812849670648575,2.827620029449463,2.8803861141204834,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.30069147679023445,2.7565391063690186,2.795513391494751,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,0.5957843884825706,2.762259006500244,2.775705099105835,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,0.8907927555264905,2.7680208683013916,2.754681348800659,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,1.1860768415499479,2.768228530883789,2.7322161197662354,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.003838135045953095,2.8538012504577637,2.915804386138916,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.30442549008876085,2.762644052505493,2.7952933311462402,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,0.6003142540575936,2.765726089477539,2.774374008178711,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,0.8956747320480645,2.7657041549682617,2.752279758453369,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,1.1914672401035205,2.770334005355835,2.7266628742218018,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.003928342019207776,2.7415614128112793,2.9035792350769043,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.30377443809993565,2.7571301460266113,2.7941505908966064,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,0.5996992298169062,2.7596471309661865,2.7737717628479004,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,0.8948479648679495,2.7611215114593506,2.7543280124664307,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.003837354015558958,2.9216513633728027,2.948066234588623,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.3002632629359141,2.767704486846924,2.79465389251709,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,0.5975335380062461,2.7674591541290283,2.773294687271118,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,0.8931891599204391,2.766742467880249,2.750622272491455,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,1.1885220900876448,2.7847108840942383,2.727613925933838,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,1.4838406912749633,2.7870473861694336,2.7002882957458496,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,1.7784341832157224,2.813202142715454,2.669952392578125,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.0038122079567983747,2.944727897644043,3.0076651573181152,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.3007602137513459,2.755558490753174,2.796403408050537,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,0.5970948624890298,2.760558605194092,2.773742914199829,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,0.8929739192826673,2.768475294113159,2.74883770942688,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,1.188720651436597,2.7736551761627197,2.7197790145874023,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.003925436991266906,2.9034273624420166,2.9819958209991455,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.300994700868614,2.751063108444214,2.7970309257507324,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,0.5972207316663116,2.7573256492614746,2.7768170833587646,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,0.893382215872407,2.7526848316192627,2.7571663856506348,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,1.1902179480530322,2.7605907917022705,2.735032558441162,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.005837957956828177,2.8285486698150635,2.9058547019958496,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.30331838177517056,2.756692886352539,2.794666051864624,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,0.5990129249403253,2.7627625465393066,2.774714946746826,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,0.8949944932246581,2.7640492916107178,2.7529654502868652,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,1.1906633619219065,2.764407157897949,2.7289509773254395,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.003893136978149414,2.8104257583618164,2.870044231414795,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.3000535691389814,2.7555012702941895,2.7934329509735107,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,0.5955568130593747,2.7630748748779297,2.773076295852661,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,0.8917391321156174,2.75762677192688,2.7519643306732178,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,1.1879201512783766,2.756791353225708,2.7269864082336426,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.003944722935557365,3.0511364936828613,3.081282615661621,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.3008067823247984,2.7604916095733643,2.803534507751465,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,0.5973528155591339,2.759836196899414,2.782660484313965,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,0.8939547534100711,2.7593822479248047,2.762765645980835,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,1.1894609103910625,2.7512545585632324,2.738814353942871,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,1.4854066719999537,2.769763231277466,2.711881160736084,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,1.7810598721262068,2.7850894927978516,2.6808812618255615,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",70,2.0799203319475055,2.8149497509002686,2.645266056060791,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.02153595897834748,2.979677677154541,2.887665271759033,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.5064867860637605,2.9433765411376953,2.7673676013946533,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,0.9657739871181548,2.9778995513916016,2.7315144538879395,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,1.4241405020002276,2.9805002212524414,2.696702718734741,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,1.8874396269675344,3.0230321884155273,2.6620028018951416,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.0056056169560179114,2.9870333671569824,2.9436140060424805,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.46557163272518665,2.939700126647949,2.7717504501342773,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,0.9260371827986091,2.9590330123901367,2.7328107357025146,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,1.3842548097018152,2.9803366661071777,2.6962122917175293,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,1.841267237556167,3.044170379638672,2.655958652496338,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.005586882005445659,2.9648561477661133,2.9051766395568848,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.4668354948516935,2.9492459297180176,2.7688307762145996,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,0.9263946969294921,2.972292423248291,2.7271156311035156,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,1.3856594058452174,3.0184340476989746,2.687805414199829,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,1.8455777718918398,3.083101272583008,2.6490561962127686,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.005932523054070771,2.978719711303711,2.91367244720459,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.4659184841439128,2.9456989765167236,2.767699956893921,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,0.9244162032846361,2.9612183570861816,2.728654623031616,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,1.3827810023212805,3.0102765560150146,2.6919193267822266,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,1.8439740802859887,3.0717368125915527,2.65480375289917,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.005717884982004762,3.1192245483398438,3.0101706981658936,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.47218767274171114,2.950160026550293,2.773038864135742,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,0.9373479939531535,2.9586753845214844,2.7354774475097656,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,1.401977353147231,2.9923739433288574,2.698784828186035,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,1.8685386702418327,3.0661020278930664,2.6623470783233643,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.00555703800637275,3.0067379474639893,2.9657862186431885,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.4709902572212741,2.9375247955322266,2.7758562564849854,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,0.9358270493103191,2.9486918449401855,2.7394864559173584,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,1.3984000191558152,2.990057945251465,2.7041730880737305,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,1.8622184580890462,3.013016700744629,2.666842460632324,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.00579543795902282,3.173474073410034,3.0828006267547607,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.48057787539437413,2.9307854175567627,2.771923065185547,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,0.952324690297246,2.948974132537842,2.7313528060913086,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,1.4227983182063326,2.9728434085845947,2.695669651031494,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,1.8932358352467418,3.0322980880737305,2.658825159072876,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.0058096450520679355,3.0395541191101074,2.95633602142334,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.4767925958149135,2.943359851837158,2.766895294189453,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,0.9480181187391281,2.965123414993286,2.731605052947998,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,1.4176816896069795,2.9938554763793945,2.694003105163574,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,1.8892646215390414,3.063403606414795,2.65572190284729,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.005769901094026864,3.208510637283325,3.0451853275299072,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.4776102511677891,2.9540624618530273,2.7653379440307617,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,0.9495436120778322,2.971475601196289,2.7252602577209473,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,1.419792382977903,3.0150952339172363,2.6889350414276123,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,1.89306098385714,3.0557518005371094,2.655540943145752,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.005691446014679968,3.0793800354003906,3.007991313934326,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.4895691490964964,2.943326234817505,2.768148422241211,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,0.9624338850844651,2.968809127807617,2.72763991355896,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,1.432565609109588,3.002661943435669,2.6898000240325928,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,1.9020123560912907,3.0605928897857666,2.6482648849487305,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.004521751077845693,2.988675117492676,3.0021350383758545,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.25558493204880506,2.834524631500244,2.7941970825195312,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,0.5118719678139314,2.852219343185425,2.7541167736053467,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,0.7612170457141474,2.868797779083252,2.713829517364502,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,1.0104748777812347,2.888312339782715,2.680387020111084,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.0023604989983141422,2.8989627361297607,2.8942627906799316,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.2512810459593311,2.835171699523926,2.795045852661133,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,0.5038257790729403,2.854257583618164,2.754962921142578,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,0.7521491018123925,2.881782054901123,2.7161686420440674,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,0.9997927541844547,2.907132148742676,2.6748907566070557,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.0023731630062684417,2.9138317108154297,2.955709934234619,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.2512740151723847,2.8342204093933105,2.792405128479004,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,0.4993578133871779,2.840883493423462,2.749107599258423,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,0.7497312023770064,2.864818572998047,2.6997334957122803,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,0.9979731112252921,2.9109115600585938,2.6484601497650146,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.0024343469412997365,2.9349896907806396,2.9468324184417725,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.25077392091043293,2.8294451236724854,2.796360731124878,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,0.49796212976798415,2.846046209335327,2.755815029144287,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,0.7453301837667823,2.868762731552124,2.7119898796081543,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,0.9934665989130735,2.8914682865142822,2.6639153957366943,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.002440227079205215,2.851391077041626,2.880413055419922,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.2507299472345039,2.8328189849853516,2.779310464859009,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,0.4985561172943562,2.8563191890716553,2.736222743988037,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,0.7458238851977512,2.8852202892303467,2.6999707221984863,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,0.9930211614118889,2.9181318283081055,2.659519672393799,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.002448091981932521,2.905277967453003,2.903982162475586,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.250408150954172,2.850240707397461,2.7881386280059814,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,0.5000731871696189,2.8676795959472656,2.7507190704345703,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,0.7475905143655837,2.8791985511779785,2.7124204635620117,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,0.9945664999540895,2.9053354263305664,2.676292896270752,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.0024438840337097645,2.8826839923858643,2.890333652496338,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.25128788803704083,2.840651750564575,2.785970687866211,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,0.49866170110180974,2.862213373184204,2.7440600395202637,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,0.7459793700836599,2.8803703784942627,2.7073686122894287,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,0.99342487717513,2.9050488471984863,2.6721787452697754,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.002493274980224669,2.888627529144287,2.903470516204834,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.251048945938237,2.8327441215515137,2.7945163249969482,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,0.4980884586693719,2.838831901550293,2.7540037631988525,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,0.7455044214148074,2.864203929901123,2.7193446159362793,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,0.9926481184083968,2.8832101821899414,2.667545795440674,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.0023201050935313106,2.8815243244171143,2.899775743484497,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.24950250098481774,2.8287668228149414,2.7860312461853027,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,0.49632632499560714,2.8527026176452637,2.738104820251465,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,0.7457810490159318,2.8861262798309326,2.687472105026245,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,0.9927520441124216,2.931098222732544,2.670832872390747,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.002590755932033062,2.8983662128448486,2.9019110202789307,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.25151709374040365,2.848973274230957,2.7897467613220215,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,0.49912038957700133,2.862966537475586,2.7465734481811523,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,0.7462894254131243,2.883964776992798,2.705371379852295,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,0.9929602073971182,2.9135210514068604,2.6704814434051514,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.017158554983325303,3.055833101272583,2.954455614089966,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.7042837121989578,3.02984619140625,2.716651678085327,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,1.3681906322017312,3.099257469177246,2.631213665008545,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,2.036762019386515,3.2156941890716553,2.5246925354003906,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,2.6993107434827834,3.2628228664398193,2.470684289932251,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.00458462699316442,3.2969493865966797,3.156888961791992,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.668835285701789,3.0570199489593506,2.7167272567749023,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,1.3303707686718553,3.1145968437194824,2.6338119506835938,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,1.9929212598362938,3.1821537017822266,2.5373754501342773,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,2.65449654776603,3.2756357192993164,2.4490463733673096,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.004539163084700704,3.0369973182678223,2.923767328262329,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.6694316092180088,3.0428876876831055,2.7096364498138428,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,1.3301744965137914,3.083375930786133,2.6204051971435547,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,1.99038420652505,3.1529293060302734,2.5185186862945557,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.0047219309490174055,3.039229154586792,2.9838640689849854,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.665857171989046,3.007988929748535,2.7021007537841797,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,1.3264850940322503,3.087582588195801,2.6149487495422363,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,1.9906529171857983,3.1494743824005127,2.5201687812805176,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,2.651163694448769,3.2989861965179443,2.445138931274414,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.004508336074650288,3.193464517593384,3.1277942657470703,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.6663185791112483,3.0064897537231445,2.715477228164673,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,1.3288268530741334,3.0696749687194824,2.6345725059509277,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,1.9891838042531163,3.1576876640319824,2.537652015686035,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,2.650625095353462,3.232201099395752,2.511673927307129,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.004469664068892598,3.3361823558807373,3.2084176540374756,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.6682980521582067,3.017787218093872,2.7215464115142822,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,1.328725073253736,3.0667386054992676,2.630539894104004,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,1.9889834221685305,3.1482737064361572,2.518937349319458,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,2.650701893144287,3.2040085792541504,2.4885196685791016,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.0045475190272554755,2.973686695098877,2.936490535736084,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.6655229239258915,3.0220882892608643,2.717623233795166,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,1.3259042908903211,3.0717380046844482,2.6138975620269775,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,1.9860638080863282,3.1312978267669678,2.5340428352355957,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.004359740996733308,3.210092306137085,3.1196682453155518,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.6692004122305661,3.0208325386047363,2.7154743671417236,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,1.3294027582742274,3.078117847442627,2.633124828338623,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,1.9899994834559038,3.1325180530548096,2.5347955226898193,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,2.6503225216874853,3.1941070556640625,2.473224639892578,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.004617337952367961,3.124181032180786,3.0337073802948,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.6676467360230163,3.0413990020751953,2.7148141860961914,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,1.327830604976043,3.126110792160034,2.631316661834717,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,1.9891849911073223,3.23016357421875,2.5366201400756836,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,2.649401591042988,3.2922821044921875,2.4686832427978516,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.004533963976427913,3.178487539291382,3.0253474712371826,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.6668780549662188,3.0308597087860107,2.713853359222412,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,1.3307398748584092,3.097618579864502,2.6296677589416504,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,1.9927479738835245,3.1729393005371094,2.519869327545166,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,2.653734781080857,3.2439770698547363,2.445835590362549,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.008260192000307143,3.1942734718322754,3.136338233947754,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,1.0973861160455272,2.970508575439453,2.65867280960083,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,2.1803274201229215,3.112478494644165,2.5340662002563477,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,3.2605385582428426,3.2453627586364746,2.467522621154785,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,4.338717174134217,3.3108177185058594,2.388303518295288,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.006645041052252054,3.0764472484588623,3.0553064346313477,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,1.0856683930614963,2.994065999984741,2.666670322418213,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,2.1667475568829104,3.084822177886963,2.5615592002868652,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,3.246167420176789,3.1938629150390625,2.4625346660614014,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,4.3284024860477075,3.243701457977295,2.3985204696655273,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.00670218700543046,3.2683491706848145,3.2992701530456543,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,1.085789425065741,2.969407796859741,2.711552619934082,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,2.1646646310109645,3.026939630508423,2.5594372749328613,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,3.24301950505469,3.15330171585083,2.498232841491699,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,4.324878957006149,3.195096015930176,2.3867974281311035,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.006601791945286095,3.046731472015381,3.076071262359619,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,1.0886929319240153,2.979201316833496,2.6563539505004883,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,2.1709799729287624,3.10066819190979,2.51969051361084,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,3.252557361032814,3.192218780517578,2.4354333877563477,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,4.337623688043095,3.358304977416992,2.3050522804260254,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.006639030994847417,3.3721935749053955,3.2745580673217773,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,1.0849029856035486,2.923948287963867,2.680955410003662,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,2.1626318705966696,3.0362677574157715,2.5419981479644775,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,3.2389463474974036,3.166851043701172,2.423323631286621,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,4.316593941301107,3.3033175468444824,2.342033863067627,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.006602893001399934,3.3421199321746826,3.3082659244537354,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,1.08518946391996,2.9648542404174805,2.6778368949890137,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,2.1610134817892686,3.0020065307617188,2.571883201599121,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,3.238212523749098,3.1296491622924805,2.4761929512023926,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,4.313786328653805,3.277570962905884,2.3639345169067383,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.00676609599031508,3.410360813140869,3.42842435836792,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,1.0850006559630856,2.9359278678894043,2.6744604110717773,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,2.1602628239197657,3.042330741882324,2.551771640777588,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,3.236056584864855,3.1375560760498047,2.4619369506835938,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,4.313697842764668,3.258950710296631,2.3916707038879395,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.006638298975303769,3.137239933013916,3.0406131744384766,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,1.08237178332638,2.990342617034912,2.6599066257476807,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,2.1574205881915987,3.042201280593872,2.5261409282684326,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,3.2325321722310036,3.128660202026367,2.4517815113067627,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,4.309220445225947,3.177819013595581,2.3422176837921143,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.006745226914063096,3.1452863216400146,3.1294610500335693,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,1.082227254868485,2.975161552429199,2.6724226474761963,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,2.1565386328147724,3.0716845989227295,2.5371577739715576,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,3.231109060929157,3.1800332069396973,2.4487485885620117,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,4.31004332494922,3.2944586277008057,2.324568033218384,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.0067556260619312525,3.432025194168091,3.343993902206421,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,1.084719582926482,2.9637725353240967,2.68636155128479,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,2.163904872140847,3.059429883956909,2.567316770553589,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,3.240887350169942,3.106812000274658,2.470370054244995,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,4.318302961182781,3.246959686279297,2.3956522941589355,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.10540786699857563,2.9128873348236084,2.9602138996124268,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,0.3704712080070749,2.85813045501709,2.8740689754486084,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,0.653923862031661,2.849357843399048,2.844917058944702,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,0.9271319699473679,2.857588768005371,2.829989433288574,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,1.2245859549148008,2.907275915145874,2.937628746032715,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,1.514192139962688,2.8677046298980713,2.7758493423461914,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.07887090998701751,2.8770968914031982,2.909268617630005,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,0.49131262593436986,2.917656421661377,2.9404799938201904,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,0.663261728012003,2.8759660720825195,2.8829848766326904,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,0.9243861469440162,2.8407812118530273,2.8267245292663574,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,1.3852750610094517,2.8421852588653564,2.7716050148010254,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,1.4939104040386155,2.8464930057525635,2.7606008052825928,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",60,1.7735656580189243,2.893739938735962,2.7577712535858154,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.07953076204285026,2.872681140899658,2.914182186126709,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,0.36256765597499907,2.8479084968566895,2.8690812587738037,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,0.6564652359811589,2.8418638706207275,2.8353307247161865,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,0.9245155859971419,2.8508481979370117,2.828601598739624,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,1.2400149770546705,2.8432633876800537,2.7812271118164062,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,1.4844014599220827,2.891296863555908,2.7390081882476807,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.07870120590087026,2.8535139560699463,2.908646583557129,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,0.371213682112284,2.9082648754119873,2.9248738288879395,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,0.6536586809670553,2.8945024013519287,2.892712354660034,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,0.9354259250685573,2.8619401454925537,2.8421833515167236,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.0793902319855988,2.8788838386535645,2.9209506511688232,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,0.36316107504535466,2.8511438369750977,2.8595681190490723,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,0.6515143510187045,2.990112781524658,3.016634941101074,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,0.9255865690065548,2.867372751235962,2.840604305267334,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,1.2092618059832603,2.9211695194244385,2.9344987869262695,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.07891928998287767,2.940420389175415,2.993786573410034,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,0.37855387094896287,2.892559289932251,2.9197142124176025,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,0.6507892180234194,2.848666191101074,2.859727144241333,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,1.1000619679689407,2.859138011932373,2.841437816619873,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,1.2252932840492576,2.8569698333740234,2.766289710998535,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,1.5223888870095834,2.852731227874756,2.775913715362549,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.07851954200305045,2.9603171348571777,2.989370107650757,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,0.36181656492408365,2.9046380519866943,2.9246578216552734,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,0.6414909560699016,2.86981201171875,2.860584259033203,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,0.9335330899339169,2.8754143714904785,2.8514280319213867,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,1.2200577730545774,2.872648000717163,2.8146414756774902,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,1.4887998000485823,2.8422727584838867,2.7460250854492188,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",60,1.7898232340812683,2.8859519958496094,2.7376885414123535,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",70,2.079363001976162,2.891387462615967,2.6979854106903076,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",80,2.359452561940998,2.916743516921997,2.7031636238098145,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.07868660299573094,2.9014108180999756,2.9421756267547607,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,0.37184789997991174,2.8548178672790527,2.879702091217041,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,0.6652312489459291,2.8393170833587646,2.841024398803711,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,1.1150704440660775,2.840582847595215,2.816481590270996,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,1.2081775820115581,2.894221305847168,2.9233062267303467,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,1.4887650550808758,2.8531532287597656,2.7602388858795166,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.07914879801683128,2.9099671840667725,2.9374611377716064,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,0.36049866303801537,2.902446746826172,2.9388718605041504,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,0.6430031680501997,2.8400161266326904,2.835320472717285,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,0.9249685329850763,2.9419209957122803,2.9654994010925293,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,1.2151765490416437,2.8515727519989014,2.7863128185272217,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,1.4874074090039358,2.8673224449157715,2.7763712406158447,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.0787024210439995,2.862269163131714,2.9035730361938477,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,0.3606759919784963,2.9137988090515137,2.9228034019470215,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,0.655067628948018,2.8561370372772217,2.8582546710968018,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,1.1136810589814559,2.8595988750457764,2.8332884311676025,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,1.211980327963829,2.851715087890625,2.794516086578369,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,1.4800465230364352,2.844156265258789,2.755331516265869,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",60,1.8153500580228865,2.9051411151885986,2.7249808311462402,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",70,2.0525006259558722,2.895592451095581,2.6994142532348633,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",80,2.3496978449402377,2.9150609970092773,2.6784489154815674,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.14339382806792855,3.1333963871002197,3.192140579223633,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,0.6380913810571656,2.845161199569702,2.83156156539917,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,1.141101422952488,2.841309070587158,2.8059775829315186,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,1.6273761730408296,2.887066125869751,2.7657663822174072,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,2.17538584000431,2.9060399532318115,2.7411532402038574,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,2.596185839967802,2.9422383308410645,2.7070884704589844,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.13409472408238798,3.0406830310821533,3.0669193267822266,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,0.6273250608937815,2.8391196727752686,2.830151319503784,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,1.2857210109941661,3.458749294281006,3.4685747623443604,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,1.618350033997558,2.870304584503174,2.7858822345733643,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,2.1251319580478594,2.8929924964904785,2.759732246398926,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.1296758729731664,2.9941272735595703,3.042966604232788,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,0.638813097961247,2.8354830741882324,2.8210623264312744,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,1.1382979230256751,2.846250534057617,2.8052845001220703,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,1.6511412729742005,2.8731391429901123,2.770829439163208,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,2.119962470023893,3.559546709060669,3.5679893493652344,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.12911215599160641,3.275609254837036,3.3099112510681152,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,0.6376995421014726,2.837902784347534,2.83168888092041,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,1.1439697849564254,2.851640224456787,2.8081564903259277,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,1.619999271002598,3.571838617324829,3.5790112018585205,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,2.299289007089101,2.9086151123046875,2.7560365200042725,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.1285843470832333,3.128417730331421,3.1767516136169434,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,0.6265678980853409,2.8527402877807617,2.8276443481445312,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,1.161040140897967,2.8418586254119873,2.800257682800293,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,1.6309509209822863,2.8712868690490723,2.7748260498046875,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,2.0716021029511467,2.9010496139526367,2.7430171966552734,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,2.585334451054223,2.925122022628784,2.733109474182129,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.1288093440234661,3.0758168697357178,3.1187784671783447,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,0.613038252107799,2.844883918762207,2.825918436050415,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,1.1032335730269551,2.8568923473358154,2.799208402633667,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,1.5779741519363597,3.5783817768096924,3.5837926864624023,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,2.0757452289108187,2.887930154800415,2.74448299407959,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.12806178897153586,3.026228189468384,3.098435640335083,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,0.6116050970740616,2.844334363937378,2.827660322189331,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,1.2775387079454958,2.8482038974761963,2.801238775253296,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,1.5747577029978856,2.8669140338897705,2.7851550579071045,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,2.0733127769781277,2.908538579940796,2.7323100566864014,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.12821519805584103,3.1567299365997314,3.203435182571411,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,0.6219891420332715,2.8232555389404297,2.827660322189331,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,1.112225780962035,2.848297119140625,2.8041133880615234,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,1.5997595670633018,2.868889808654785,2.7818100452423096,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,2.079455236089416,2.8934645652770996,2.7507717609405518,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.12913997599389404,3.0583932399749756,3.133298873901367,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,0.6109762679552659,2.8425776958465576,2.827005386352539,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,1.1196164030116051,2.8450634479522705,2.8131895065307617,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,1.582770584966056,2.8639285564422607,2.777148962020874,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,2.2092588770901784,2.8923325538635254,2.7563583850860596,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.12961827998515218,3.1502585411071777,3.198873996734619,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,0.6115875879768282,2.847338914871216,2.8315420150756836,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,1.0901419169967994,3.352754592895508,3.364717483520508,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,1.5715337740257382,2.8734374046325684,2.773343563079834,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,2.075335605069995,2.886721134185791,2.753359079360962,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.23275302606634796,3.3997182846069336,3.439145803451538,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,0.9481440589297563,2.8581113815307617,2.8333306312561035,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,1.8694597420981154,2.8563504219055176,2.7985785007476807,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,2.487014604965225,2.883359909057617,2.768181800842285,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,3.2885507150785998,4.210505962371826,4.23092794418335,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,3.919031116995029,2.8938944339752197,2.724546432495117,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.20244542800355703,3.315854787826538,3.366523265838623,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,1.1104301830055192,2.8678524494171143,2.828655242919922,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,1.7238220979925245,2.86651349067688,2.796419858932495,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,2.4336746719200164,2.900320053100586,2.761958122253418,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,3.3824890449177474,2.9024362564086914,2.7319839000701904,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,4.036932006012648,2.921231508255005,2.708501100540161,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.19530648307409137,3.421353578567505,3.462359666824341,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,0.948090331046842,2.872511386871338,2.823328733444214,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,1.6965728800278157,2.866659641265869,2.7993361949920654,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,2.621383249061182,2.8898885250091553,2.754277229309082,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,3.1179624079959467,2.887629270553589,2.7334866523742676,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,3.975121950963512,2.8996999263763428,2.7226343154907227,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.1936533300904557,4.381227970123291,4.399751663208008,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,0.9259982099756598,2.8635685443878174,2.8309378623962402,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,1.6575123619986698,2.8725812435150146,2.7991511821746826,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,2.5291085250210017,2.8949570655822754,2.750774621963501,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,3.0866815720219165,2.9126548767089844,2.7324471473693848,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.1863234449410811,3.2244386672973633,3.2626423835754395,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,0.9216539789922535,2.861250400543213,2.831954002380371,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,1.8257440329762176,2.869483709335327,2.7910704612731934,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,2.4028743719682097,2.8687655925750732,2.765556812286377,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,3.272166057024151,2.8745720386505127,2.7552785873413086,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.18721442006062716,3.6656360626220703,3.6874327659606934,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,0.9164604350226,2.8618242740631104,2.8293545246124268,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,1.6350403970573097,2.885066509246826,2.7950806617736816,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,2.5420565609820187,2.880340099334717,2.76857328414917,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,3.129614180070348,2.885809898376465,2.7485368251800537,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.33645585901103914,3.3843183517456055,3.4321253299713135,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,1.080031066085212,2.8809401988983154,2.837765693664551,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,1.6720763869816437,2.8663442134857178,2.796851396560669,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,2.374797831987962,2.8643298149108887,2.7738380432128906,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,3.2166328930761665,2.878851890563965,2.7351901531219482,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,3.9790197029942647,2.9366464614868164,2.7019124031066895,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",60,4.532494621933438,2.9325547218322754,2.67183780670166,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.1904688311042264,3.4433434009552,3.484018325805664,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,1.0623463590163738,2.8561575412750244,2.8375473022460938,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,1.6578594140009955,2.8672478199005127,2.796121120452881,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,2.3750276670325547,2.879943609237671,2.7675724029541016,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,3.220977970981039,2.9003517627716064,2.7360219955444336,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.1957438720855862,3.368842124938965,3.439082622528076,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,0.9172824500128627,2.8719165325164795,2.8240203857421875,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,1.8395813719835132,2.876431941986084,2.7924861907958984,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,2.3492495589889586,2.885158061981201,2.765228271484375,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,3.0728160440921783,2.9053776264190674,2.7391653060913086,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.19492413406260312,3.3995678424835205,3.426159381866455,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,1.0520826380234212,2.8598270416259766,2.832578182220459,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,1.645112783065997,2.868755578994751,2.790848970413208,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,2.360497213085182,2.9013359546661377,2.7593207359313965,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,3.2548640460008755,2.914916515350342,2.7373547554016113,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.19902682991232723,2.799560308456421,2.8537466526031494,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,0.529070726945065,2.8019111156463623,2.8444275856018066,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,0.9148473590612411,2.8138303756713867,2.8926424980163574,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,1.3477666840190068,2.8945698738098145,2.688960552215576,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.1038183739874512,2.811856985092163,2.8470797538757324,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,0.5118862229865044,2.802086591720581,2.838146924972534,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,0.9157883799634874,2.8570613861083984,2.7377846240997314,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,1.3267154729692265,2.917475700378418,2.7045888900756836,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,1.7581282689934596,2.956334114074707,2.6522765159606934,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.10309963393956423,2.8636069297790527,2.8866913318634033,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,0.5124514099443331,2.8157026767730713,2.867771625518799,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,0.9155677820090204,2.800410032272339,2.841521978378296,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,1.4839363619685173,2.875074863433838,2.721417188644409,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,1.7512684440007433,2.906696319580078,2.6678175926208496,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,2.138892572023906,2.9376652240753174,2.5908923149108887,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.10351116396486759,2.8079614639282227,2.867263078689575,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,0.510072267963551,2.8127284049987793,2.7848236560821533,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,0.9224227059166878,2.866605281829834,2.7429709434509277,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,1.3303247139556333,2.91811466217041,2.6992294788360596,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.10392921906895936,2.855426073074341,2.8889405727386475,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,0.5128298560157418,2.81417179107666,2.7912776470184326,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,0.9184098519617692,2.8764963150024414,2.7276015281677246,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,1.3276871968992054,2.808506727218628,2.856416702270508,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,1.7356889189686626,2.804694652557373,2.855699300765991,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,2.3250737689668313,2.9683096408843994,2.596771240234375,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",60,2.5464107480365783,3.0132699012756348,2.5752477645874023,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",70,2.958819204941392,3.07513165473938,2.501150369644165,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.10567729792091995,2.7928643226623535,2.8482165336608887,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,0.5093272170051932,2.814032793045044,2.884127616882324,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,0.9190719689941034,2.8060436248779297,2.8584742546081543,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,1.3352757160319015,2.8838236331939697,2.7090811729431152,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.1122265710728243,2.81097674369812,2.834998607635498,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,0.519895076053217,2.8284378051757812,2.7847061157226562,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,0.9223726829513907,2.847440719604492,2.753460168838501,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,1.3341361749917269,2.8109638690948486,2.8372342586517334,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,1.887313403072767,2.786161184310913,2.8450193405151367,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,2.1599430579226464,2.915862798690796,2.629218578338623,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",60,2.546488074003719,2.8025295734405518,2.8665759563446045,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",70,2.967507224995643,3.0604019165039062,2.5260605812072754,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.10396071500144899,2.8013460636138916,2.8723206520080566,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,0.5185276379343122,2.8086628913879395,2.7877511978149414,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,0.9735708190128207,2.8541245460510254,2.7572789192199707,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,1.3424236400751397,2.8691318035125732,2.7168452739715576,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.10342577693518251,2.815725326538086,2.856463670730591,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,0.5059646819718182,2.805206775665283,2.784618854522705,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,0.9028569670626894,2.8883330821990967,2.7366998195648193,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,1.3110559970373288,2.899393081665039,2.716547727584839,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,1.708227185998112,2.8908417224884033,2.704434394836426,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.10216548794414848,2.8276708126068115,2.8464341163635254,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,0.5035182849969715,2.819087505340576,2.7888073921203613,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,0.9114485749742016,2.8402085304260254,2.7455894947052,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,1.3030346999876201,2.897339344024658,2.6972336769104004,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","simulated[seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,1.7077432019868866,2.904100179672241,2.6526975631713867,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.43520562804769725,2.965071678161621,2.8676681518554688,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,1.159116734052077,3.035226345062256,2.9472522735595703,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,2.0897679090267047,3.0330324172973633,2.9559407234191895,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,3.0356751630315557,3.0627763271331787,2.593993663787842,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.2189791010459885,3.002696990966797,2.916598081588745,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,1.1485860979883,3.0937957763671875,2.976480484008789,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,2.0858902449253947,3.0228540897369385,2.6507835388183594,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,3.004879459971562,3.12740159034729,2.5425381660461426,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.22045319201424718,3.077333450317383,2.9618637561798096,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,1.3068857890320942,2.989607334136963,2.762986898422241,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,2.06201489106752,3.0229110717773438,2.618288516998291,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,2.986219749087468,3.0342462062835693,2.5540876388549805,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,3.9007006310857832,3.1711008548736572,2.415152072906494,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.21824196493253112,3.0347084999084473,2.902592420578003,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,1.1381499930284917,2.9447507858276367,2.722074508666992,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,2.0560927389888093,3.0205111503601074,2.6349732875823975,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,2.977222969988361,3.136871814727783,2.5375773906707764,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,3.8804885849822313,3.1064090728759766,2.4271738529205322,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.2165471150074154,3.1302971839904785,2.9682767391204834,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,1.142911391099915,2.9691455364227295,2.713831901550293,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,2.065695900004357,3.0203499794006348,2.646421432495117,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,2.9791960960719734,3.0598580837249756,2.9092769622802734,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,4.10217120393645,3.1403093338012695,2.5105838775634766,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.21749896206893027,3.0906941890716553,2.922778367996216,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,1.1415720890508965,2.987241268157959,2.725940704345703,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,2.0809132289141417,3.0383753776550293,2.68739652633667,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,2.982029279926792,3.0865895748138428,2.5919132232666016,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,3.922733064973727,3.1778528690338135,2.446133613586426,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.21908020798582584,2.984990358352661,2.8991599082946777,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,1.1408372450387105,2.9655601978302,2.729391098022461,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,2.0455348959658295,3.019595146179199,2.9466664791107178,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,2.9553677069488913,3.07893967628479,2.9733128547668457,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,3.880126175004989,3.2057690620422363,2.4238522052764893,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.34667215205263346,3.122208595275879,2.9958975315093994,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,1.1321083781076595,3.094158172607422,2.9542369842529297,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,2.069779383018613,3.0154972076416016,2.6433897018432617,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,2.978259518975392,3.07560658454895,2.5264456272125244,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,3.8820539159933105,3.120786666870117,2.487943172454834,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,4.829292722977698,3.232814311981201,2.312730550765991,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.21772802202031016,2.9787745475769043,2.876523494720459,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,1.3056794700678438,2.9703423976898193,2.734326124191284,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,2.048134901910089,3.020449638366699,2.638420343399048,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,2.99763149197679,3.0231008529663086,2.570202112197876,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,3.8853677039733157,3.130948066711426,2.422302007675171,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.21776625304482877,3.075495958328247,2.958070993423462,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,1.134040687000379,3.021117687225342,2.940120220184326,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,2.067738192039542,3.039820432662964,2.6035497188568115,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,2.9801175110042095,3.112887144088745,2.477715253829956,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","simulated[seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,3.8968039469327778,3.141024589538574,2.438546895980835,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.34775323001667857,2.9808454513549805,2.912264108657837,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,1.8639405650319532,3.0722532272338867,2.9959990978240967,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,3.3705118959769607,3.0330491065979004,2.5379302501678467,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,5.064838842954487,3.102771043777466,2.498763084411621,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.3553439130773768,2.981299638748169,2.852689743041992,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,1.8806353209074587,2.959646224975586,2.6709203720092773,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,3.2342665530741215,3.03039813041687,2.970155954360962,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,4.836996449972503,3.10514497756958,2.4259755611419678,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,6.111726264003664,3.275684356689453,2.356726884841919,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.3416097079170868,3.078554391860962,3.0289530754089355,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,1.9284452970605344,3.1665945053100586,3.1204757690429688,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,3.2518790339818224,3.006685733795166,2.5554347038269043,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,4.664531865972094,3.121894121170044,2.4234941005706787,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,6.257034181966446,3.010317802429199,2.9231371879577637,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,7.693720838986337,3.2806777954101562,2.2362146377563477,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.33292910689488053,3.0233683586120605,2.959867238998413,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,1.7870503809535876,2.9143519401550293,2.677495002746582,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,3.220827341079712,3.000415325164795,2.537407398223877,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,4.806902401032858,3.1251368522644043,2.461820125579834,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,6.09330296004191,3.217200756072998,2.258988380432129,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.33688207808882,2.9469003677368164,2.91512393951416,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,1.779833622975275,2.965334892272949,2.7061657905578613,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,3.350519997999072,3.039609670639038,3.0125508308410645,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,4.665318847983144,2.9446098804473877,2.949918746948242,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,6.258197559975088,3.223191499710083,2.327871561050415,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,7.57846529094968,3.240363121032715,2.196408987045288,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",60,9.108489366946742,3.4584052562713623,2.0645205974578857,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.3328175060451031,3.0354466438293457,2.959381103515625,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,1.7824723590165377,2.945667266845703,2.6755154132843018,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,3.362388451001607,3.022984504699707,2.554497241973877,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,4.672107276972383,3.030777931213379,2.4683380126953125,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,6.224696260062046,3.178070068359375,2.263127565383911,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.34147809795103967,2.9268999099731445,2.8719680309295654,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,1.7712844159686938,2.9458470344543457,2.6961560249328613,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,3.2069068180862814,3.0341217517852783,2.5424249172210693,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,4.9154606310185045,3.072155475616455,2.478938341140747,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.3403369339648634,3.007664203643799,2.943591594696045,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,1.7846684069372714,2.975952386856079,2.6963837146759033,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,3.2201062469976023,2.998636484146118,2.5434212684631348,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,4.796621809946373,3.085385322570801,2.5048751831054688,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,6.08613814401906,3.118574619293213,2.338549852371216,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.3396731249522418,3.0676634311676025,2.998180389404297,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,1.7719661890296265,2.9631004333496094,2.6615841388702393,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,3.370867996942252,2.964236259460449,2.5703423023223877,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,4.662770151975565,3.10015606880188,2.427558183670044,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,6.202228693990037,3.2073564529418945,2.301006317138672,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.34037390199955553,2.969320774078369,2.9195024967193604,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,1.7678649009903893,2.953646183013916,2.675368309020996,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,3.190760525991209,3.023775100708008,2.5743801593780518,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,4.749579413910396,3.0528862476348877,2.4141125679016113,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","simulated[seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,6.057017606915906,3.1653409004211426,2.2870326042175293,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","simulated[seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.0011265570065006614,3.0085458755493164,2.98710298538208,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","simulated[seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.17890317284036428,2.8603854179382324,2.827237606048584,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","simulated[seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,0.3082842550938949,2.860128402709961,2.7919678688049316,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","simulated[seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,0.42899264907464385,2.9082300662994385,2.777454137802124,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","simulated[seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,0.5495398852508515,2.9673404693603516,2.750898838043213,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","simulated[seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,0.6699609471252188,2.9918549060821533,2.6864240169525146,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","simulated[seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.001177971949800849,2.8950932025909424,2.8733177185058594,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","simulated[seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.12277083797380328,2.873239517211914,2.836488962173462,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","simulated[seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,0.2513374218251556,2.8796379566192627,2.8016774654388428,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","simulated[seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,0.3725350158056244,2.9059436321258545,2.779972553253174,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","simulated[seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,0.49345948197878897,2.9611611366271973,2.728604793548584,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","simulated[seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.0013029839610680938,2.8469960689544678,2.8227059841156006,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","simulated[seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.12319184676744044,2.8844566345214844,2.8338701725006104,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","simulated[seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,0.2442660618107766,2.9459145069122314,2.8428843021392822,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","simulated[seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,0.37417407776229084,2.8717682361602783,2.6843056678771973,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","simulated[seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.00107989099342376,3.0012946128845215,2.977689743041992,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","simulated[seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.12229436310008168,2.865621328353882,2.8310468196868896,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","simulated[seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,0.2462102600838989,2.876779794692993,2.8051841259002686,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","simulated[seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,0.3662808850640431,2.954063892364502,2.8164496421813965,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","simulated[seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,0.4862094728741795,2.9093546867370605,2.684426784515381,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","simulated[seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.001141924993135035,2.810690402984619,2.787400722503662,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","simulated[seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.12176038383040577,2.8769376277923584,2.8341119289398193,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","simulated[seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,0.24200826871674508,2.908125400543213,2.8179516792297363,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","simulated[seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,0.36224216676782817,2.928285598754883,2.768073320388794,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","simulated[seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.0010152910836040974,2.9766390323638916,2.9505016803741455,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","simulated[seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.12279677588958293,2.8670296669006348,2.822758674621582,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","simulated[seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,0.24658273404929787,2.8803515434265137,2.8018791675567627,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","simulated[seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,0.3675257069990039,2.8585739135742188,2.7221574783325195,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","simulated[seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,0.48830037598963827,2.9183154106140137,2.6933417320251465,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","simulated[seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,0.6089054802432656,2.9677977561950684,2.638040065765381,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","simulated[seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,0.7296688093338162,2.984722852706909,2.5687146186828613,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","simulated[seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.0012842090800404549,3.023922920227051,3.0019237995147705,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","simulated[seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.12226111313793808,2.8670201301574707,2.8271548748016357,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","simulated[seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,0.2429579570889473,2.879804849624634,2.807582139968872,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","simulated[seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,0.36326320725493133,2.868171453475952,2.7329981327056885,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","simulated[seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,0.483499537454918,2.880168914794922,2.6659224033355713,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","simulated[seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.0011184620670974255,2.849825143814087,2.823732376098633,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","simulated[seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.1215040871175006,2.8748466968536377,2.835639238357544,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","simulated[seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,0.2427922380156815,2.8866639137268066,2.8012948036193848,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","simulated[seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,0.3629281928297132,2.9796230792999268,2.8055784702301025,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","simulated[seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.0010574290063232183,2.8024306297302246,2.779163360595703,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","simulated[seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.12192607019096613,2.8678438663482666,2.8250131607055664,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","simulated[seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,0.24244446330703795,2.9219813346862793,2.839308738708496,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","simulated[seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,0.3627515950938687,2.9030330181121826,2.7592670917510986,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","simulated[seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.0012679989449679852,2.929631471633911,2.905541181564331,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","simulated[seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.12199880683328956,2.8680965900421143,2.833467483520508,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","simulated[seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,0.2418802585452795,2.8853092193603516,2.816707134246826,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","simulated[seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,0.3621590557741001,2.8853511810302734,2.729728937149048,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","simulated[seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,0.4827668669167906,2.973568916320801,2.7269718647003174,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.0025606340495869517,2.7798235416412354,2.8240158557891846,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.18204032420180738,2.8205971717834473,2.8425729274749756,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,0.35677441535517573,2.9380478858947754,2.8368935585021973,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,0.5317522233817726,2.8746726512908936,2.612319231033325,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.0013626349391415715,2.6677663326263428,2.711960554122925,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.17909056099597365,2.819432020187378,2.850048065185547,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,0.36123487690929323,2.826162576675415,2.779384136199951,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,0.5364736089250073,3.030186176300049,2.8034191131591797,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.001456117955967784,2.860568046569824,2.9049551486968994,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.17781591485254467,2.795670986175537,2.8197407722473145,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,0.3516604646574706,2.9000697135925293,2.8058807849884033,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,0.525474306778051,2.7832913398742676,2.529193162918091,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,0.6990102039417252,3.1750378608703613,2.7148537635803223,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,0.8772268559550866,3.0920183658599854,2.3813424110412598,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,1.0547797550680116,3.603776216506958,2.698512554168701,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.0016946500400081277,2.880183696746826,2.9244320392608643,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.17806194291915745,2.8065927028656006,2.8371081352233887,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,0.35524450975935906,2.898235321044922,2.7801132202148438,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,0.5311086318688467,2.963317394256592,2.6460561752319336,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,0.7074653280433267,3.0902624130249023,2.550875663757324,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.0016076889587566257,2.713184356689453,2.7573800086975098,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.17787035985384136,2.8131580352783203,2.8489267826080322,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,0.35345343977678567,2.828915596008301,2.7637791633605957,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,0.529037672909908,3.107468843460083,2.8899178504943848,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.001441259984858334,2.735436201095581,2.779914379119873,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.17775695712771267,2.8216769695281982,2.846076011657715,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,0.35268818179611117,2.9413702487945557,2.8664939403533936,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,0.5269770760787651,3.1598591804504395,2.82718563079834,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.0013911770656704903,2.868377923965454,2.9129414558410645,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.17729724710807204,2.8048243522644043,2.8356354236602783,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,0.3521020171465352,2.869595527648926,2.711838722229004,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,0.5274794900324196,2.9371516704559326,2.582225799560547,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,0.701968734851107,3.201662063598633,2.6589808464050293,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.0016140699153766036,2.704033851623535,2.7485384941101074,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.17697235685773194,2.813369035720825,2.834906578063965,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,0.3530161537928507,2.8573482036590576,2.743290424346924,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,0.5288438779534772,3.1517961025238037,2.86657977104187,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.0015380189288407564,2.686213970184326,2.730551242828369,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.18272106687072664,2.8087120056152344,2.842731475830078,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,0.35858132306020707,2.816195011138916,2.6853580474853516,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,0.5320101549150422,2.9819066524505615,2.665292978286743,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.0014796110335737467,2.7061288356781006,2.750413417816162,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.1768256640061736,2.79974627494812,2.831684112548828,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,0.3504450669279322,2.828324317932129,2.7239203453063965,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","simulated[seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,0.5236589601263404,3.085864305496216,2.7992091178894043,null,null,null,null,null,null,null,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",0,0.0032324569765478373,7.198835372924805,7.205682277679443,0.9682028591632843,0.010151442978018331,2.0492570251226425,0.2943901247053673,0.2648676261305809,0.1358413808264609,1.008373955031857,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",10,0.482774460921064,6.333890914916992,6.343031883239746,0.9533089324831963,0.016699260191529345,1.424641266465187,0.20824315456418516,0.13511722348630428,0.09156873229465193,2.0048086589667946,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",20,0.955056439852342,6.192237854003906,6.141057014465332,0.9530327469110489,0.01518029952045003,1.424846589565277,0.16294957383887898,0.12172026745975018,0.08499323530413108,2.2016167000401765,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",30,1.4253969237906858,5.9944024085998535,5.968912124633789,0.9504095315933228,0.013977930932326205,1.3806085661053658,0.15565028818890367,0.11507152207195759,0.08892396725244918,2.0863732380094007,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",40,1.8980574574088678,5.8695220947265625,5.841623783111572,0.9530964829027653,0.015116685834099442,1.3676491230726242,0.1647941663265644,0.11080658063292503,0.08327507037701287,2.0200361449969932,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",50,2.3694744645617902,5.7135467529296875,5.713547229766846,0.9456172771751881,0.014764554857324367,1.2889561653137207,0.14245924471866125,0.09474349394440651,0.07400253314155143,2.1136230250122026,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",60,2.841178852599114,5.622426986694336,5.6334381103515625,0.9441226311028004,0.019870047736660013,1.2816285267472267,0.14208957439082293,0.09893948957324028,0.0751919324651711,2.1115006260806695,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",70,3.3191075024660677,5.506093502044678,5.524972438812256,0.9458013251423836,0.01840915214979513,1.2847287878394127,0.1495682145762593,0.09859266132116318,0.07626548275388492,2.1036412010435015,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",80,3.7915170536143705,5.3871917724609375,5.404134273529053,0.9404303319752216,0.01741120714822899,1.2176618799567223,0.12647642374550933,0.08720187842845917,0.07143156279701282,2.028309482964687,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",90,4.255692872451618,5.283026218414307,5.25527286529541,0.9378085322678089,0.01823300985460338,1.218233399093151,0.13245735544071777,0.09015971049666405,0.07224816309860417,2.133957118028775,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",100,4.725136324530467,5.160472393035889,5.1312055587768555,0.9351509474217892,0.02151740309705478,1.173287644982338,0.17091402595675284,0.0850653313100338,0.06771743207840192,2.096446118084714,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",110,5.198259898577817,5.071746826171875,5.0041890144348145,0.9312132596969604,0.022590659150984398,1.1347736790776253,0.12073802909319628,0.08218676596879959,0.06528264836360027,2.1236036439659074,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",120,5.666725741699338,4.901256561279297,4.835681915283203,0.9269425682723522,0.02111182662979169,1.101626668125391,0.1243755639752613,0.0751299262046814,0.06972029966460926,2.1982685830444098,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",130,6.140143624856137,4.821472644805908,4.685371398925781,0.9192518182098866,0.0243409435216288,1.05077688023448,0.12238418888290209,0.07265748083591461,0.06903958195570332,2.172463270020671,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",140,6.618098101927899,4.703082084655762,4.5201520919799805,0.9144601635634899,0.026984900150485467,1.0357540659606457,0.10857385502317517,0.071208655834198,0.06465651330012713,2.198177576996386,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",150,7.092791061731987,4.570213317871094,4.4095988273620605,0.9052417427301407,0.0360924358993807,0.9863829836249352,0.1558350240734908,0.06815716624259949,0.05938615765706079,2.2040131110697985,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",160,7.5634514768607914,4.444158554077148,4.250909328460693,0.9015500992536545,0.03425284862506169,0.9682410657405853,0.13319223783349876,0.06546076759696007,0.06040002788258676,2.407142366981134,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",170,8.034201621892862,4.488665580749512,4.2018842697143555,0.8973381631076336,0.03799980618396737,0.9562801606953144,0.12729784965595042,0.06335742771625519,0.05828728024400716,2.4054456929443404,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",180,8.507572529953904,4.3943281173706055,4.149660110473633,0.8979498632252216,0.040380466862654917,0.9498890079557896,0.13663991643563098,0.06348202377557755,0.05890057159772919,2.367365457001142,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",190,8.981569043826312,4.382473945617676,4.016433238983154,0.884030569344759,0.04439714747878302,0.8931718431413174,0.1393905050534016,0.055982742458581924,0.05060043994455847,2.4358187910402194,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",200,9.454479249776341,4.26389741897583,3.9302380084991455,0.8850701339542866,0.04393963393702056,0.896847777068615,0.11694700905652512,0.05771072208881378,0.04878508477845074,2.462002038024366,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",210,9.927809649845585,4.236172676086426,3.8676867485046387,0.8812537975609303,0.04763494101432232,0.9001166373491287,0.1770745775989426,0.06339874118566513,0.058428627824075444,2.5767373929265887,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",220,10.396489453851245,4.183010101318359,3.795876979827881,0.8761887922883034,0.04570317994795104,0.8615004345774651,0.1761623503967151,0.052053097635507584,0.045946801010466,2.6118183030048385,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",230,10.864763989695348,4.154912948608398,3.7242584228515625,0.8742982558906078,0.042644817342686374,0.8392585292458534,0.11763779896736186,0.0473238080739975,0.04319634041901936,2.6059443230042234,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",240,11.335154857835732,4.043769836425781,3.6569066047668457,0.8753961212933064,0.04654134305464564,0.8676857650279999,0.14933608936229611,0.05650296062231064,0.0498813670598173,2.675364676048048,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",250,11.80846629687585,3.990377187728882,3.6087348461151123,0.8768017590045929,0.045117595654452856,0.8723040595650673,0.17468076743563132,0.05636170506477356,0.04695480887807392,2.5127856290200725,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",260,12.277689898735844,3.9273416996002197,3.5118961334228516,0.8754245154559612,0.03962459782413353,0.8813933357596397,0.12313666105722897,0.053909461945295334,0.042227385103284505,2.5874968729913235,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",270,12.747819638578221,3.865987777709961,3.460458755493164,0.870299432426691,0.044861312896856194,0.8471188843250275,0.17782591835001893,0.047276031225919724,0.035533180065038715,2.6828355860197917,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",280,13.21730553964153,3.845303535461426,3.3971948623657227,0.8668189086019993,0.0469056879596273,0.8315457962453365,0.16422702463639133,0.04968686029314995,0.04583532263494641,2.7350090210093185,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",290,13.69040678278543,3.8570990562438965,3.4121124744415283,0.8713047839701176,0.046095875156780895,0.8672955334186554,0.16032720565240696,0.05551571771502495,0.046498971004196324,2.806595496018417,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",300,14.161709970911033,3.692436695098877,3.321913003921509,0.8675865456461906,0.0534595619950995,0.8459957502782345,0.1971935392725735,0.05051829665899277,0.042964247045290595,2.821692730067298,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",310,14.63673172192648,3.672248363494873,3.268099784851074,0.8699626512825489,0.043157943109126955,0.8568968139588833,0.14639140316548457,0.05331040546298027,0.04290465127586013,2.778157224995084,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",320,15.10430051467847,3.6980066299438477,3.216731548309326,0.867612861096859,0.04592880742748835,0.8059140406548977,0.1897184266207517,0.04806143790483475,0.047861094877952905,2.7897937269881368,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",330,15.574865561560728,3.5957696437835693,3.1680734157562256,0.8629459105432034,0.052098016730567166,0.8535085134208202,0.19005130709372586,0.05107872188091278,0.038504062471643744,2.8006554229650646,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",340,16.044648337643594,3.6187312602996826,3.1200332641601562,0.8672162182629108,0.0418350658884042,0.826182883232832,0.16766716457632116,0.046618688851594925,0.032535526602403386,2.7210308560170233,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",350,16.515094825765118,3.6141700744628906,3.1171414852142334,0.854584950953722,0.05162253806662264,0.8167369924485683,0.20223827352033993,0.0473274327814579,0.034382056847811995,2.7769515609834343,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",360,16.980738316779025,3.5653176307678223,3.060246229171753,0.858886931091547,0.047597419946774695,0.8042274489998817,0.17076222841905317,0.043012041598558426,0.03106872541463847,2.9184526599710807,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",370,17.45431244710926,3.518592596054077,3.048801898956299,0.860384114086628,0.04802589718339196,0.7615508139133453,0.17108147628385087,0.037611689418554306,0.024180646627568355,2.8190612560138106,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",380,17.929179496131837,3.5783956050872803,3.024888753890991,0.859492439776659,0.05086817423206306,0.8257738836109638,0.17281719467291676,0.04839178919792175,0.040959893193864866,2.8241378020029515,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",390,18.39914928912185,3.5356993675231934,2.960514783859253,0.8602294363081455,0.05095051799760509,0.8287140354514122,0.22885901567441824,0.049945808947086334,0.04069718995452161,2.813140209997073,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",400,18.869718729169108,3.4496278762817383,2.936457633972168,0.8531800545752048,0.04897154052567483,0.8027985617518425,0.1917549610400391,0.04731631651520729,0.03917361841009913,2.9934230259386823,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",410,19.345024838112295,3.450432062149048,2.875645160675049,0.8530548177659512,0.04348761623262819,0.7686551548540592,0.1647375031434071,0.04046162962913513,0.031583958692275725,2.6669693420408294,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",420,19.81312320707366,3.4647927284240723,2.850466251373291,0.859926950186491,0.050404451783551504,0.7900355979800224,0.1824044607462693,0.03963077440857887,0.02361885395705127,2.9358410560525954,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",430,20.286874252953567,3.44726824760437,2.818838119506836,0.8573616482317448,0.05059558330400771,0.7797382697463036,0.19518269716208075,0.0420939102768898,0.03185511936142105,2.8896567000774667,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",440,20.76438843004871,3.5186715126037598,2.8580498695373535,0.8535766899585724,0.04549892950883012,0.7875485755503178,0.1507039683288621,0.04247402399778366,0.029522672350635572,3.022638057009317,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",450,21.236907265265472,3.4584507942199707,2.781479597091675,0.8536390736699104,0.04340466997277605,0.7591391950845718,0.19058983840326912,0.03803306445479393,0.028932887547199495,2.932331406045705,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",460,21.711480800178833,3.4016335010528564,2.7883718013763428,0.8589482754468918,0.05079117473351404,0.7927168346941471,0.17906034191517334,0.04404541105031967,0.031917530587675505,2.9232778450241312,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",470,22.190704177017324,3.4072952270507812,2.7379579544067383,0.8584295436739922,0.0517992504203732,0.7637124359607697,0.18142465008028472,0.03850993514060974,0.023581745703051955,3.028598622069694,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",480,22.664390872814693,3.3981852531433105,2.707005739212036,0.8517121598124504,0.04652711257655352,0.7394656967371702,0.17555428000917492,0.03526081144809723,0.025124201743058808,3.0075699740555137,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",490,23.141240217955783,3.3692126274108887,2.7008249759674072,0.8522388786077499,0.047170922956109064,0.7872873544692993,0.22050765997908975,0.04204815253615379,0.02605217015002393,3.0590099419932812,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",500,23.609340744791552,3.2687454223632812,2.6195015907287598,0.8514996469020844,0.053417681595021665,0.7685230318456888,0.2007153386233179,0.0419444777071476,0.02818773147689577,3.005984144983813,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",510,24.085313472896814,3.3326733112335205,2.6502068042755127,0.8464995697140694,0.04948604348695685,0.7783556431531906,0.208400025441352,0.04251248016953468,0.03272552667033694,2.888058310956694,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",520,24.561098718782887,3.349933385848999,2.6411125659942627,0.8439973257482052,0.0501499223312066,0.7401615716516972,0.20743168529652822,0.03426406532526016,0.021309005735267502,2.944749257992953,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",530,25.0345938737737,3.3142199516296387,2.5789363384246826,0.8460101969540119,0.05478440470585404,0.7466756161302328,0.20553792538436486,0.03578564524650574,0.023467234165758247,2.980022980016656,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",0,0.0014082100242376328,7.057709693908691,7.082895755767822,0.9655454233288765,0.010366051820495829,1.94392891228199,0.27048010998815103,0.24160543270409107,0.1405032554248558,0.9552190480753779,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",10,0.4801238109357655,6.27508544921875,6.326099395751953,0.9549282155930996,0.012426491542383107,1.4770061671733856,0.18748145537870345,0.14141515083611012,0.09141255136842844,1.8082437620032579,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",20,0.9516693638870493,6.131115436553955,6.1093010902404785,0.955320306122303,0.01334667555382338,1.4664576947689056,0.1852491522217502,0.13164517655968666,0.0877541637512612,2.152387263951823,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",30,1.4210957917384803,5.954901218414307,5.941605091094971,0.9516299441456795,0.0157601930058257,1.4004239588975906,0.155049243162303,0.11805475689470768,0.08545019052738342,2.0062086860416457,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",40,1.8890772983431816,5.822772979736328,5.807703495025635,0.9509599544107914,0.016629874845490774,1.3527941182255745,0.1560048668447835,0.10698318481445312,0.07565506464492965,2.0060553199145943,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",50,2.3543906490085647,5.706711292266846,5.710787296295166,0.9482435509562492,0.015760744197373947,1.3511762768030167,0.17377561246249013,0.1118973158299923,0.08466936763844224,2.0832482139812782,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",60,2.8195976079441607,5.591688632965088,5.559728145599365,0.9427218325436115,0.018977171865336956,1.2769662886857986,0.15880802612256908,0.09871034696698189,0.07610124105348519,2.059496880043298,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",70,3.293049842817709,5.48527717590332,5.444512367248535,0.9435741491615772,0.018157984689920303,1.2387829944491386,0.12793657931660002,0.08786478266119957,0.07054675045588998,2.05765357601922,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",80,3.7684366109315306,5.384635925292969,5.3289079666137695,0.9393622912466526,0.02089907489104156,1.1966946050524712,0.12349271363526934,0.08315771073102951,0.06858116944779867,2.0744233729783446,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",90,4.235436409711838,5.278004169464111,5.193573474884033,0.9382349103689194,0.018814749173776773,1.1611483730375767,0.12810099578256134,0.07465298101305962,0.06050381210990497,2.1311537029687315,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",100,4.702096067019738,5.211091041564941,5.0753173828125,0.9355190396308899,0.019120529308640413,1.1746200025081635,0.12320436654757363,0.0813625417649746,0.062259361400416866,2.1745203899918124,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",110,5.169914531055838,5.058064937591553,4.9184746742248535,0.9328631684184074,0.022345085200112826,1.1242989599704742,0.1124127181788718,0.07606729865074158,0.06032484396692521,2.097689673071727,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",120,5.632011068868451,4.965179443359375,4.800839424133301,0.9296281114220619,0.02521373587632036,1.0923004448413849,0.15330488298801195,0.07732880860567093,0.06425811712634613,2.130982397007756,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",130,6.098502754815854,4.840157985687256,4.651007652282715,0.9240743294358253,0.026244673298129913,1.0656336806714535,0.1233310859254931,0.06970785558223724,0.055898445595658576,2.1608492250088602,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",140,6.564655973925255,4.817812442779541,4.5604963302612305,0.9186726287007332,0.024049263379615955,1.047521773725748,0.17317997943627253,0.0732513926923275,0.06353865261253197,2.2682786439545453,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",150,7.031586282537319,4.660680770874023,4.449733734130859,0.9154976457357407,0.029629882219664384,0.9863291792571545,0.151798114672817,0.06316780298948288,0.05307542102842529,2.2847220060648397,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",160,7.504484471515752,4.560248374938965,4.294757843017578,0.9081119894981384,0.03206104554022514,0.9791906289756298,0.14722735171062362,0.06890822574496269,0.059950978471378674,2.2860731369582936,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",170,7.978171658585779,4.491917610168457,4.210513591766357,0.9030778519809246,0.03857878682833092,0.973681140691042,0.13123059057634104,0.06742604449391365,0.04955426618046077,2.3326170860091224,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",180,8.453054157900624,4.431977272033691,4.1476945877075195,0.8975507654249668,0.038390188394625685,0.9676251225173473,0.1570343150765434,0.06863671913743019,0.05818456501260749,2.3219851839821786,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",190,8.931692724814638,4.389585494995117,4.104828357696533,0.8944110386073589,0.041368011629630615,0.9216663464903831,0.1618037829874132,0.06256648898124695,0.058190125832037594,2.35817472205963,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",200,9.408225260791369,4.317311763763428,4.018978118896484,0.8914487212896347,0.04018823597207085,0.9171379208564758,0.13330060969782073,0.059926100075244904,0.053630577720337844,2.3566822248976678,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",210,9.869850902003236,4.223113059997559,3.91562557220459,0.8837562724947929,0.04503573542088001,0.9056066907942295,0.16458038294520472,0.05926942452788353,0.052168808288542076,2.474640373024158,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",220,10.338141704094596,4.126054763793945,3.841715097427368,0.8822628147900105,0.046190316873431544,0.8972268104553223,0.16773413966147876,0.05857224762439728,0.048733693672620516,2.5215272849891335,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",230,10.806033879052848,4.133983612060547,3.788357973098755,0.8834238760173321,0.042533097705109595,0.8856746517121792,0.138848259557865,0.056900136172771454,0.04823849701953826,2.4807374300435185,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",240,11.270290381507948,4.085134983062744,3.737891435623169,0.8840084373950958,0.04756794334932362,0.8667729943990707,0.14475994574276846,0.05368060618638992,0.04772978756765508,2.456639255047776,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",250,11.736973287304863,3.958866596221924,3.611508369445801,0.8780818097293377,0.04801102624481498,0.8762828446924686,0.1586890573964698,0.05455242097377777,0.04034430490878173,2.513009706977755,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",260,12.205326225142926,3.9942150115966797,3.5884082317352295,0.8741158545017242,0.04272514982943264,0.8473611585795879,0.10624113906531787,0.049478814005851746,0.03946271209780758,2.506015673978254,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",270,12.673281783005223,3.9283580780029297,3.5179738998413086,0.8746621832251549,0.04925958631498074,0.8586916625499725,0.1559185253402547,0.05388808250427246,0.04689143111399447,2.5152328859549016,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",280,13.141657075029798,3.8843231201171875,3.524994134902954,0.879241231828928,0.045678137077805755,0.8612104468047619,0.16651258416112633,0.05364951863884926,0.04491290590838371,2.576055414043367,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",290,13.607350122998469,3.7486653327941895,3.406144380569458,0.8734128437936306,0.04464810381064704,0.8551274910569191,0.1461215056014543,0.052403274923563004,0.03848600680547614,2.5827390919439495,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",300,14.083142956253141,3.7102062702178955,3.3377232551574707,0.8722837492823601,0.04716575735188235,0.8505369685590267,0.17153450139418822,0.05241912603378296,0.040791310642585545,2.602368599968031,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",310,14.553665665211156,3.611071825027466,3.262289524078369,0.871217355132103,0.03943048159407207,0.8460610993206501,0.13996624023186294,0.05040692910552025,0.0400597510487077,2.6682079839520156,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",320,15.023501515272073,3.554736614227295,3.1756954193115234,0.8649584092199802,0.05438283793925705,0.8082478605210781,0.1468524732811057,0.046638067811727524,0.03540555627956421,2.5931155449943617,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",330,15.490351822343655,3.5411221981048584,3.162198781967163,0.8699965700507164,0.042943026007804475,0.8638943806290627,0.19225929646910697,0.052902646362781525,0.04107289979621819,2.6298886849544942,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",340,15.95748129452113,3.461273193359375,3.0956509113311768,0.8694418966770172,0.04070028897498363,0.8523021377623081,0.19664724242912024,0.05558612197637558,0.043827722964021466,2.657943945028819,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",350,16.42609927651938,3.43017840385437,3.027261972427368,0.8597428314387798,0.040084874761296775,0.8061240091919899,0.14125672731661065,0.04924428090453148,0.0387554814251494,2.6256392010254785,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",360,16.893096457235515,3.377732753753662,2.969935894012451,0.8584292531013489,0.04601321285599669,0.8041614517569542,0.15079615075964745,0.04293778911232948,0.03334577123284301,2.6788313180441037,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",370,17.36108963296283,3.4121365547180176,2.9788808822631836,0.8519589677453041,0.03811716285023905,0.7774877361953259,0.12105276638351334,0.03935275599360466,0.028688250290297455,2.7665059430291876,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",380,17.82857121399138,3.28240966796875,2.903954029083252,0.8505242504179478,0.03990572373598174,0.8004996664822102,0.15794726214754848,0.0433943010866642,0.0259678028910454,2.749985091970302,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",390,18.29115485807415,3.2814548015594482,2.849304437637329,0.8541580475866795,0.04599432783068215,0.7975524477660656,0.17300444484802188,0.047208067029714584,0.038701821208756225,2.7383136389544234,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",400,18.755509112146683,3.30855655670166,2.819427013397217,0.8534264788031578,0.03638421009212098,0.7786542847752571,0.11925067224791738,0.04010377079248428,0.03169774369288528,2.8630918140988797,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",410,19.223233274067752,3.18375563621521,2.733700752258301,0.8500965349376202,0.0437631707007689,0.8191707693040371,0.21179136960304906,0.04740777611732483,0.033983880254698375,2.729345944011584,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",420,19.690615118131973,3.1170663833618164,2.7358686923980713,0.8489378429949284,0.037096557888946446,0.7740630656480789,0.1537060972989271,0.040247660130262375,0.032832231756078877,2.733451449079439,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",430,20.160951527301222,3.0963993072509766,2.6989552974700928,0.8463793322443962,0.039950312414224115,0.7781939301639795,0.18699020560811877,0.044538822025060654,0.035754895691554954,2.792271245038137,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",440,20.63188204623293,3.0660300254821777,2.666775703430176,0.8495233729481697,0.032193051886983085,0.8063826449215412,0.1717567253074777,0.04603352025151253,0.035425744026800585,2.860108623979613,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",450,21.100860332371667,3.075316905975342,2.6056807041168213,0.8564438819885254,0.04124283582422421,0.8049302846193314,0.17338691844310172,0.04434766620397568,0.03149735032040079,2.8448611949570477,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",460,21.5707924234448,3.094135046005249,2.640052080154419,0.8400575406849384,0.04117179544129547,0.7724712789058685,0.18235213430858047,0.04185384511947632,0.03331860888117457,2.802218928001821,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",470,22.045777724706568,3.0913209915161133,2.5403642654418945,0.8446586728096008,0.044633213451845836,0.7264802902936935,0.1650129634918623,0.03531690686941147,0.02469819640739103,2.950371799059212,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",0,0.001477877958677709,7.082309722900391,7.099628925323486,0.963870458304882,0.011587186933125599,1.9497764483094215,0.26230184282169483,0.24336694180965424,0.14589210432035768,0.9132081889547408,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",10,0.4731778126442805,6.299445629119873,6.330450057983398,0.9523328766226768,0.015645758742340043,1.4939253106713295,0.20230496144653337,0.13496117293834686,0.08885912432437985,1.96443414199166,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",20,0.9428631404880434,6.093367099761963,6.098785877227783,0.9500745348632336,0.01606898221102067,1.4284781217575073,0.181838823351924,0.12763818353414536,0.0891531510887316,2.1679188649868593,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",30,1.412850824301131,5.938417911529541,5.961588382720947,0.9517825990915298,0.0160454996723721,1.4098928049206734,0.17576428519634799,0.12180067226290703,0.08750935299479629,2.0918323600199074,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",40,1.8835725429235026,5.81932258605957,5.831243991851807,0.9496187120676041,0.014145110872496775,1.3615251630544662,0.15928868333188786,0.1101429034024477,0.08141550044537739,2.0707082269946113,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",50,2.351821813150309,5.712323188781738,5.725401878356934,0.9462273344397545,0.017256233901327925,1.3072669953107834,0.15780502954629225,0.10163052007555962,0.07477422320158389,2.0774987740442157,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",60,2.8208052201662213,5.660278797149658,5.636375904083252,0.9455575682222843,0.018566834302246742,1.3156091570854187,0.16781869106302028,0.10235951840877533,0.0776423462512551,2.0891332819592208,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",70,3.2887246793834493,5.518411636352539,5.5168232917785645,0.9442447312176228,0.020363107082921047,1.2943681254982948,0.17659533517637876,0.10436750203371048,0.07939360272248924,2.079608800006099,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",80,3.7599220593692735,5.425324440002441,5.381947040557861,0.9403416998684406,0.018367594447329667,1.2372542098164558,0.1504073648742847,0.08957060799002647,0.06886833128382004,2.064961110940203,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",90,4.2293741626199335,5.2807207107543945,5.260687351226807,0.9386613741517067,0.01713395257935342,1.2274283021688461,0.1711800385587025,0.0905781053006649,0.0696264346532953,2.069538409006782,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",100,4.7061688548419625,5.211278438568115,5.116447448730469,0.9353038370609283,0.016657254122371533,1.1792998686432838,0.152763577551406,0.08700726553797722,0.06719120323191426,2.150914642959833,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",110,5.181042765732855,5.044258117675781,4.9666032791137695,0.9307859763503075,0.01816238696270242,1.177838608622551,0.15256057808842438,0.08576981723308563,0.0682442631397238,2.1922819560859352,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",120,5.644907447625883,4.924313545227051,4.790088653564453,0.9253240413963795,0.022763419007894282,1.106065221130848,0.14128919279722726,0.07977752387523651,0.06479530886778777,2.1593628900591284,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",130,6.107867879676633,4.739317893981934,4.626587867736816,0.9162292033433914,0.02277943371584663,1.063875112682581,0.13674778142410393,0.07780248299241066,0.06299368916954295,2.2532418030314147,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",140,6.574803285882808,4.640178203582764,4.487835884094238,0.9154968224465847,0.025233298723425328,1.0756391324102879,0.15221786298516835,0.08196620270609856,0.06593136349733185,2.2584238150157034,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",150,7.049303424078971,4.5712890625,4.357263565063477,0.9080818742513657,0.02695282532255679,1.0076600164175034,0.14853278238943513,0.07365087419748306,0.06704744004598374,2.2091789779951796,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",160,7.517813726095483,4.419332504272461,4.2293195724487305,0.9046946801245213,0.029307447144532362,0.9723817892372608,0.14062289682241208,0.06952157616615295,0.065511012383481,2.284728816943243,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",170,7.985883408924565,4.381484031677246,4.1730637550354,0.8993552774190903,0.03367418343419318,0.9588968530297279,0.13895545393951594,0.07308102399110794,0.06428974273825293,2.3402105619898066,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",180,8.460280234809034,4.368758201599121,4.056211948394775,0.8959070295095444,0.03566955899162587,0.9656453989446163,0.1531825381111991,0.07169145345687866,0.06591599914734587,2.34455897891894,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",190,8.93438351561781,4.263092517852783,3.9693174362182617,0.8889487981796265,0.037125019433968044,0.9030336327850819,0.13185764668229027,0.06437655910849571,0.06182389361316681,2.3578680811915547,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",200,9.403189069475047,4.235344409942627,3.9252755641937256,0.8867803551256657,0.036981026044387195,0.9387838132679462,0.15663571487381556,0.06788412854075432,0.05940007292285467,2.5012930419761688,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",210,9.877137742820196,4.191376686096191,3.803574800491333,0.8819883428514004,0.03963162703159396,0.8973966427147388,0.12033368073980859,0.062475696206092834,0.05824011086615212,2.563140588114038,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",220,10.350438684341498,4.139163494110107,3.7688186168670654,0.868682011961937,0.04501791052846626,0.8289414793252945,0.11183823517173574,0.05455605685710907,0.05409182528781654,2.543159032939002,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",230,10.824963444494642,4.054736137390137,3.6671085357666016,0.8746957890689373,0.03438154447586446,0.869797732681036,0.13469424389747864,0.05638206750154495,0.06059517065724372,2.5980489049106836,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",240,11.298457816126756,3.998220920562744,3.620760917663574,0.8741719089448452,0.03985063970684038,0.8649796396493912,0.12723558908280835,0.058917976915836334,0.059359171515342354,2.62706981995143,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",250,11.770457686041482,4.091895580291748,3.62503719329834,0.8785109370946884,0.03733915858624582,0.886311624199152,0.17594991204942254,0.06269539892673492,0.06666107946503208,2.637630671961233,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",260,12.240813153912313,3.9783406257629395,3.5403125286102295,0.8656640909612179,0.043697888818487754,0.8366314470767975,0.12463073557989959,0.051915284246206284,0.051327930811426066,2.3825895921327174,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",270,12.714323987136595,3.943948745727539,3.4817099571228027,0.8657214231789112,0.039806612033803354,0.8551933206617832,0.1113607472222188,0.05541890487074852,0.05239542006051321,2.7006049731280655,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",280,13.18727417557966,3.887624979019165,3.409986734390259,0.8619379475712776,0.043493325982171906,0.846230112016201,0.12813237788234272,0.05462810769677162,0.052739971615454004,2.716277515050024,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",290,13.656062810565345,3.921227216720581,3.3922982215881348,0.8574546799063683,0.040544024788033865,0.7984650060534477,0.10064922806250062,0.0474078431725502,0.0474914069950175,2.6837925100699067,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",300,14.130710554192774,3.8699941635131836,3.34329891204834,0.8652663379907608,0.0421060121740552,0.8157854080200195,0.10655729002948079,0.051164839416742325,0.0492526436563819,2.723377436865121,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",310,14.600187205825932,3.8326518535614014,3.357804775238037,0.8670083954930305,0.034988193008457964,0.8290654309093952,0.11035665660995087,0.05160975083708763,0.05175787416614282,2.6830679459962994,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",320,15.068289550603367,3.7888059616088867,3.274261474609375,0.8580944761633873,0.040000245319703226,0.7846380285918713,0.12715555556746558,0.046273909509181976,0.0493032761353842,2.713102129055187,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",330,15.541246927692555,3.7904844284057617,3.199639081954956,0.857360377907753,0.03774434149765387,0.8221878558397293,0.12003111187634721,0.049898628145456314,0.0444507729507087,2.699852350866422,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",340,16.015777472988702,3.8113949298858643,3.1892495155334473,0.8488428816199303,0.05276001586140591,0.8156720027327538,0.16715866507678773,0.05179360508918762,0.04506026720476878,2.666337792063132,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",350,16.48986990272533,3.790778398513794,3.1818742752075195,0.8601706251502037,0.039176890205652695,0.8379842042922974,0.15787071687611484,0.054318178445100784,0.04574662382821302,2.690552643034607,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",0,0.0014485940337181091,7.109107494354248,7.142038345336914,0.9629529118537903,0.01358296656862821,1.9737014099955559,0.2742556081073268,0.2507504988461733,0.14911565523373685,1.0184630828443915,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",10,0.46851915516890585,6.26500940322876,6.26829195022583,0.9539197273552418,0.01634475186487198,1.4501568526029587,0.19632382481970945,0.13723128661513329,0.08620028356589823,1.9132211119867861,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",20,0.9407395529560745,6.125990390777588,6.117029666900635,0.9519642665982246,0.015750320078621535,1.425850786268711,0.19222286188430343,0.1281085666269064,0.08679356906041337,2.285053246188909,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",30,1.4113760038744658,5.99235725402832,5.961021900177002,0.9495856910943985,0.014801296110862438,1.3899325281381607,0.1503443664496885,0.12006416916847229,0.09143904550730607,2.1326481520663947,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",40,1.8810532949864864,5.877837181091309,5.841980934143066,0.9485161527991295,0.017317743057161376,1.3763495087623596,0.14385618585307716,0.11293534748256207,0.08622525508784527,2.190142798004672,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",50,2.3561714419629425,5.780012607574463,5.735074520111084,0.9452217593789101,0.018016212458859173,1.275309480726719,0.14063869581231975,0.10083922743797302,0.08088870513649013,2.168953493004665,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",60,2.8229956473223865,5.630141735076904,5.5999436378479,0.9424452073872089,0.01744292338446063,1.2601903602480888,0.15087061939963634,0.09377637505531311,0.07001124339944066,2.1571703860536218,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",70,3.2956126036588103,5.493670463562012,5.463779449462891,0.9427478536963463,0.018637332365913577,1.274959996342659,0.15390033837818215,0.09965342655777931,0.07356341895428486,2.203914434881881,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",80,3.766787442844361,5.369213104248047,5.313515663146973,0.9400949664413929,0.015819636169347107,1.230051450431347,0.1326065203612108,0.09489486366510391,0.07188852449620228,2.198861381970346,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",90,4.24007109249942,5.231817245483398,5.165984153747559,0.9343860670924187,0.01942108396694893,1.1803098917007446,0.12451392063827543,0.0877252146601677,0.0645931143232364,2.159930472029373,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",100,4.711861152667552,5.118031978607178,5.035971641540527,0.931093767285347,0.021785121108701698,1.1765837855637074,0.15917752278684705,0.08955593407154083,0.07168775084060922,2.109809034038335,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",110,5.1836253283545375,4.939115524291992,4.848405838012695,0.9228195026516914,0.02019431673057422,1.1076522655785084,0.11747127568151915,0.07913298159837723,0.06383691295843319,2.129601971944794,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",120,5.650763135403395,4.800690174102783,4.682826519012451,0.9191887564957142,0.022880706927544077,1.1072911359369755,0.14807410687880268,0.08092910796403885,0.06359132690765615,2.154055383987725,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",130,6.118811552179977,4.716733455657959,4.539556503295898,0.9084157533943653,0.030107801315319385,1.0592848509550095,0.14808170509684643,0.08114232122898102,0.06490648167721232,2.192183209117502,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",140,6.592412433121353,4.582571506500244,4.400746822357178,0.9017677716910839,0.03298878027427823,1.013489279896021,0.13285459865264704,0.07570039108395576,0.06293419305471605,2.325168153969571,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",150,7.063729792367667,4.557077407836914,4.351990222930908,0.9013975150883198,0.029402617267409673,1.0198268666863441,0.11374368605292445,0.07548162713646889,0.06107202546741693,2.3264069540891796,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",160,7.532652296824381,4.398301124572754,4.199207305908203,0.8917235732078552,0.03817987405201741,0.9647024385631084,0.1567679269213536,0.07226229831576347,0.07303571483174671,2.2843941329047084,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",170,8.006678343750536,4.3714118003845215,4.123557090759277,0.8888242468237877,0.04096042033131161,0.9564791098237038,0.14096129593045742,0.06991197541356087,0.06073286307279132,2.2565110260620713,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",180,8.477178358240053,4.264547348022461,4.030147075653076,0.8877526968717575,0.04056400478662043,0.9524785354733467,0.14618066164214025,0.07132474333047867,0.06250068440029155,2.1482035662047565,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",190,8.945895198266953,4.262528419494629,3.950650453567505,0.8817440643906593,0.04252503390961477,0.9361204989254475,0.12646295322168521,0.06384388729929924,0.05628097358863368,2.259358919924125,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",200,9.41810707631521,4.266877174377441,3.8830249309539795,0.878659799695015,0.0435289653509023,0.9070435613393784,0.11401534330882966,0.0635259710252285,0.05627948504283139,2.4212202809285372,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",210,9.887167387176305,4.082413673400879,3.7596116065979004,0.8733841478824615,0.0463220327002893,0.8878228403627872,0.14864159590847134,0.05856585502624512,0.05508126609921303,2.2889750129543245,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",220,10.356840968132019,4.007126331329346,3.6886863708496094,0.8673419691622257,0.04797851451812937,0.847038559615612,0.12962633061725323,0.057139426469802856,0.05485747332882362,2.452553658047691,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",230,10.82718194113113,3.9509902000427246,3.592148542404175,0.8671565800905228,0.04752337764734611,0.8783981241285801,0.14110730878273828,0.061663780361413956,0.05989049154784952,2.4424738381057978,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",240,11.298212377587333,3.939286708831787,3.560969114303589,0.8597126379609108,0.04864162372858077,0.8368745595216751,0.13118546746294155,0.05478689819574356,0.05330463681423793,2.436752256937325,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",250,11.766573664732277,3.8868894577026367,3.494070291519165,0.8652977980673313,0.0522238044649945,0.8599413260817528,0.1610821179060695,0.05949771776795387,0.05542106283732573,2.4143260419368744,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",260,12.237540710484609,3.87902569770813,3.4544825553894043,0.870269700884819,0.04781179069421655,0.8614346981048584,0.15175319933064552,0.05820600688457489,0.0538999075110757,2.500953927868977,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",270,12.706727677723393,3.879753589630127,3.3833115100860596,0.8656914532184601,0.04659721334228759,0.8576514273881912,0.15608825261081463,0.06056123971939087,0.05508293519553773,2.4896256898064166,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",280,13.176611974602565,3.789154291152954,3.3612143993377686,0.8584267124533653,0.05516832389348408,0.8621903359889984,0.15464957798692214,0.055504657328128815,0.04868454428660755,2.5268826929386705,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",290,13.647529416717589,3.739793300628662,3.277811288833618,0.8543394096195698,0.049842180736802764,0.8158647269010544,0.13031622882204694,0.050865933299064636,0.04704262565700735,2.3474251539446414,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",300,14.115605404833332,3.7225635051727295,3.216974973678589,0.8643802627921104,0.04230879807580543,0.856650348752737,0.14283214693538418,0.05899922177195549,0.05272251827084489,2.535466486820951,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",310,14.590221810154617,3.68167781829834,3.1929659843444824,0.8546160273253918,0.06208602061843667,0.8204205557703972,0.1681855448511526,0.05343030393123627,0.053025160416260056,2.5365741229616106,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",320,15.06167171895504,3.706207036972046,3.1640093326568604,0.8517146185040474,0.058403617922931875,0.846416037529707,0.16928661390822197,0.05728894844651222,0.056278478217959824,2.564331545960158,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",330,15.53143571340479,3.681766986846924,3.168637752532959,0.8563580885529518,0.05278657676918299,0.8206275515258312,0.1587731783440266,0.053061261773109436,0.048366708945404,2.6077316079754382,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",340,15.999256697716191,3.6388299465179443,3.0659852027893066,0.8552263826131821,0.04964008843689763,0.8167835846543312,0.13507445070214513,0.05372575670480728,0.0449906293087007,2.3695644170511514,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",350,16.46414241194725,3.5498828887939453,3.0139617919921875,0.8410977125167847,0.058056323917533154,0.8346974104642868,0.1673992013396371,0.05332934483885765,0.043963236183293154,2.652756748953834,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",360,16.93420974491164,3.580789089202881,3.0011136531829834,0.8552857264876366,0.05787050384213664,0.8250281475484371,0.17542438198357344,0.05541576072573662,0.04858179342327309,2.7585859221871942,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",370,17.40057709836401,3.557525157928467,2.948812484741211,0.8551678024232388,0.048959602282796534,0.8286148980259895,0.12862324824810575,0.05326665937900543,0.04519461080339862,2.6853061760775745,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",380,17.87070948514156,3.471066951751709,2.968006134033203,0.8522031754255295,0.0538797182673929,0.7856785506010056,0.11654105163373443,0.04574253410100937,0.04535528589147089,2.71869656490162,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",390,18.33950713579543,3.425619602203369,2.869589328765869,0.8490263223648071,0.060105007702561636,0.8321188651025295,0.18059150963626816,0.056678544729948044,0.047780235300429824,2.7098977288696915,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",400,18.812251966213807,3.4113423824310303,2.831501007080078,0.8517474979162216,0.05689078705978704,0.8490998856723309,0.1725081920127588,0.057684704661369324,0.04887514559429101,2.7543725981377065,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",410,19.281277534086257,3.4874980449676514,2.8468611240386963,0.8549460358917713,0.055021120783199685,0.8327689990401268,0.13384925192811584,0.05466606467962265,0.04729817021540615,2.7031616929452866,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",420,19.752492596628144,3.4351654052734375,2.7895588874816895,0.8490318469703197,0.052282157094396625,0.8139353357255459,0.13208169022878127,0.04778827354311943,0.03437716040189628,2.6811724540311843,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",430,20.214561042375863,3.326899766921997,2.765165328979492,0.8422203920781612,0.0639592464224328,0.7788844667375088,0.14799854909171978,0.04367808997631073,0.039562118319632966,2.7547475988976657,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",440,20.676690569147468,3.458820104598999,2.723159074783325,0.843414556235075,0.05138664952905782,0.7946382090449333,0.1776994474088829,0.053171269595623016,0.04898855090442203,2.64146287785843,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",450,21.146743605146185,3.3481006622314453,2.696866750717163,0.8494223058223724,0.05325264806401098,0.8189227730035782,0.15590734614399657,0.050830285996198654,0.04352954481774522,2.668849508976564,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",460,21.618404290638864,3.3555266857147217,2.641677141189575,0.8458293527364731,0.05526627534146072,0.8040264397859573,0.1778089165184974,0.048845075070858,0.0429279858123465,2.8511642729863524,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",0,0.0018860138952732086,7.09095573425293,7.09971809387207,0.9636530913412571,0.011769831053785838,1.9727862253785133,0.2682776698705837,0.2468578927218914,0.13987888735301543,1.0435671911109239,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",10,0.4777723760344088,6.299921989440918,6.347194671630859,0.9547730945050716,0.014276262300423373,1.443493165075779,0.2100271216850708,0.13239950314164162,0.08198584919277598,1.89289987902157,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",20,0.9496258227154613,6.098124980926514,6.124014854431152,0.9540720954537392,0.014385480406676657,1.453206792473793,0.15922620948914418,0.12605641968548298,0.0823087214949981,2.1656656339764595,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",30,1.4199027114082128,5.978829383850098,5.96461296081543,0.9509586095809937,0.01629915233365882,1.3755098655819893,0.15123183429494907,0.11170794069766998,0.08158646842273837,2.1592859958764166,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",40,1.8928620803635567,5.832439422607422,5.835360527038574,0.9482436291873455,0.016743369971721277,1.3780662342905998,0.16999097677164687,0.11395368166267872,0.07821650911931445,2.0923368439543992,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",50,2.3671040723565966,5.725576400756836,5.713602542877197,0.9458617828786373,0.017953928541194483,1.3314297646284103,0.1527961856078235,0.10749435797333717,0.08245723635811576,2.125219860114157,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",60,2.8394079417921603,5.627355098724365,5.592825889587402,0.9443689957261086,0.017203795173268577,1.298289068043232,0.13109714920443155,0.10168219543993473,0.08216898239535755,2.178479732014239,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",70,3.3112643661443144,5.514888763427734,5.5023088455200195,0.9431177265942097,0.017643663379498537,1.2370819374918938,0.121021045888598,0.09225011616945267,0.07175060432167979,2.136331130983308,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",80,3.7822219289373606,5.417243003845215,5.385828495025635,0.9386290162801743,0.01834710644048454,1.2331721857190132,0.15261581166511415,0.09260600060224533,0.07510289420398263,2.0970417228527367,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",90,4.253900982672349,5.297104835510254,5.251886367797852,0.9372269511222839,0.017607245322237557,1.1981951259076595,0.14511957859165686,0.09015646204352379,0.07180791187736653,2.1730414079502225,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",100,4.729967881226912,5.1695966720581055,5.111792087554932,0.9347540102899075,0.01727907907898327,1.1760067641735077,0.11363476724208675,0.08634316921234131,0.07401090115941159,2.1040991561021656,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",110,5.201230061240494,5.071282863616943,4.949192523956299,0.9289578981697559,0.016147158427760253,1.1332627534866333,0.1297298818462577,0.08116403967142105,0.06842576031355521,2.1955145050305873,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",120,5.674500713357702,4.934718132019043,4.821946620941162,0.9273123033344746,0.02333969926303981,1.1439189538359642,0.1499344985707506,0.09411943331360817,0.08353398868304969,2.008025927003473,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",130,6.14668502076529,4.8297882080078125,4.656885623931885,0.9188873842358589,0.024028056417390024,1.0695440359413624,0.12922933274597717,0.0773773230612278,0.06722316441432721,2.348099699942395,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",140,6.619877134682611,4.7090983390808105,4.510836601257324,0.9127502366900444,0.020666410987240338,1.038386669009924,0.11436800405322467,0.07648830488324165,0.0697708483846189,2.2460699470248073,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",150,7.093470616731793,4.6251091957092285,4.421627521514893,0.9061899222433567,0.029542498652223997,1.0270595513284206,0.12150150254510737,0.07609978318214417,0.06980871587256911,2.2476078011095524,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",160,7.566576401237398,4.537062168121338,4.314663887023926,0.8989569954574108,0.0289917302450382,0.9597834795713425,0.11493554938249265,0.06827157735824585,0.06833009000559462,2.35284806182608,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",170,8.038903197040781,4.457395553588867,4.242772102355957,0.9010623022913933,0.035725681321501467,0.9657837338745594,0.14007068291805874,0.07295433431863785,0.07517000410772395,2.3199490301776677,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",180,8.510682130232453,4.318259239196777,4.117431163787842,0.8956310823559761,0.03184221573336824,0.9653375633060932,0.15948697333235018,0.07273396104574203,0.07240327727720815,2.3752634280826896,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",190,8.98374704294838,4.314017295837402,4.04059362411499,0.8866261392831802,0.031188351200958107,0.9269801788032055,0.1417431104687477,0.06611403822898865,0.06713058143162207,2.4125880049541593,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",200,9.456140698865056,4.228225231170654,3.96427059173584,0.8915376551449299,0.03385010174257986,0.9048032984137535,0.1183381894541406,0.062458429485559464,0.061739712439535695,2.5899987739976496,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",210,9.926148527534679,4.219122886657715,3.91031551361084,0.8844284042716026,0.037655325793452446,0.9612431116402149,0.1454809818593857,0.07128239050507545,0.06662318743912941,2.521607712144032,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",220,10.39673212566413,4.183061599731445,3.826904296875,0.8807671144604683,0.0375651626166585,0.8846902959048748,0.15808237017561716,0.06463019922375679,0.06957369053166235,2.5243991918396205,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",230,10.865500424057245,4.107088088989258,3.7376174926757812,0.8806746043264866,0.03296049644440183,0.8759084455668926,0.13262159942499033,0.05923750251531601,0.06093389788732711,2.5605274259578437,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",240,11.334038136061281,4.04304838180542,3.689692497253418,0.8724953718483448,0.0387812304266837,0.8395359106361866,0.11731997449590827,0.05397038161754608,0.05456147795189805,2.56612226809375,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",250,11.80517875845544,4.022826671600342,3.6162219047546387,0.8770148009061813,0.03759947758301612,0.8439287915825844,0.12368575261597153,0.0572383813560009,0.059943210531979554,2.3787833149544895,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",260,12.275953316362575,4.05621862411499,3.639551877975464,0.8819889314472675,0.033174755017625034,0.8531150035560131,0.11792654357310939,0.05603818595409393,0.054946962685395846,2.6659912359900773,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",270,12.747178573161364,3.922595739364624,3.488405704498291,0.871033001691103,0.037295364250756524,0.8353111185133457,0.11707061674798848,0.05139276385307312,0.048767892705192904,2.594107368029654,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",280,13.220003437018022,3.8625433444976807,3.4867935180664062,0.8742987774312496,0.03322617687833629,0.8284144811332226,0.15066605359439383,0.0570853017270565,0.06614625944628844,2.46496803406626,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",290,13.692288991529495,3.7731447219848633,3.38887095451355,0.8674937300384045,0.039078136316693844,0.8168285563588142,0.13362722338085584,0.055474571883678436,0.05977033700071089,2.775186331011355,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",300,14.159938001073897,3.734356641769409,3.3329389095306396,0.8654485233128071,0.04378311120642476,0.8091969378292561,0.1343796108199276,0.053899046033620834,0.0511018166751894,2.6614013810176402,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",310,14.629343191627413,3.722595453262329,3.258732557296753,0.8594961650669575,0.04002821137320326,0.7973857149481773,0.11573268525152425,0.0500231571495533,0.05242675225871485,2.715548434993252,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",320,15.099894514074549,3.7606136798858643,3.2563881874084473,0.8694152161478996,0.031836693606955964,0.8211131580173969,0.12916736777675422,0.05421026423573494,0.0621279486270447,2.7661673268303275,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",330,15.571477517485619,3.6923813819885254,3.2441372871398926,0.8643801063299179,0.0414896654394127,0.8122300803661346,0.1214205960632583,0.05102992430329323,0.04987966354894833,2.9049708279781044,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",340,16.048641142668203,3.6619980335235596,3.1690075397491455,0.8595871105790138,0.038967851410790025,0.7881175801157951,0.11706265341179185,0.04718867316842079,0.042828512839275815,2.65142112900503,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",350,16.525832444429398,3.6860172748565674,3.1383895874023438,0.8648950420320034,0.04062776250488675,0.8168543204665184,0.1179173740994102,0.05432579293847084,0.05573490103878216,2.825356747955084,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",360,16.998826989671215,3.5023550987243652,3.0128085613250732,0.8568117506802082,0.04108871528990597,0.8160997405648232,0.12353043748974657,0.050804730504751205,0.046931979192016686,2.729510363889858,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",370,17.467861601617187,3.493304967880249,2.9819037914276123,0.8595297150313854,0.04023685730927772,0.7827936634421349,0.11818252968077515,0.04922027140855789,0.04523227651628753,2.7525291740894318,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",380,17.939771369332448,3.5084431171417236,2.9885940551757812,0.8695678003132343,0.049303374286488254,0.8235348016023636,0.15131716811581364,0.0596294030547142,0.06018489620000512,2.766031043836847,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",390,18.413373096380383,3.497938632965088,2.9512884616851807,0.8565957993268967,0.05489397524687691,0.8150853291153908,0.1448660951383153,0.05312994867563248,0.04867207523541925,2.7958325501531363,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",400,18.881599623244256,3.4555904865264893,2.9075260162353516,0.853424996137619,0.04257783976687766,0.7912671826779842,0.14106330550613058,0.04986388981342316,0.047353795975347814,2.7345040051732212,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",410,19.352682702243328,3.4768288135528564,2.8722128868103027,0.8595585599541664,0.04124306716700503,0.7928956262767315,0.1368060059853283,0.050275322049856186,0.04648114271497541,2.787737380946055,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",420,19.821237021125853,3.44718337059021,2.8413190841674805,0.8538504093885422,0.04490253860133699,0.7814564220607281,0.14378322083886014,0.04425058886408806,0.037016956969406836,2.6504322320688516,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",430,20.288104824488983,3.4835779666900635,2.8675413131713867,0.8490288630127907,0.03990244919878994,0.822246391326189,0.1320370603014684,0.05240725725889206,0.041804896829840524,2.8868362409994006,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",440,20.758247073274106,3.392421245574951,2.7264699935913086,0.8424642197787762,0.04865327606321434,0.777409490197897,0.1279852226044838,0.045356668531894684,0.03604407967965246,2.9627817759756,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",450,21.232885920908302,3.406346082687378,2.757861375808716,0.8580308184027672,0.041226447704046716,0.8007157891988754,0.15446247396209514,0.05484897643327713,0.057878579627944494,2.897196985082701,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",460,21.70253111072816,3.3724331855773926,2.6996138095855713,0.8503099493682384,0.04853048543325785,0.7955696880817413,0.16612406708461133,0.046752750873565674,0.036138746809287636,3.060681262984872,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",470,22.177930905483663,3.2930097579956055,2.7419486045837402,0.8535781800746918,0.04876073972766728,0.8396434299647808,0.20050927908452618,0.05347634106874466,0.039421314050412555,2.8571656600106508,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",480,22.6484714737162,3.34637713432312,2.682577610015869,0.8419169224798679,0.05116443251121283,0.8309789262712002,0.18341215088859356,0.05320892855525017,0.040777993361058126,3.0743255789857358,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",490,23.117260136641562,3.404557466506958,2.7043404579162598,0.8558040857315063,0.04512764936719709,0.832468468695879,0.20075466039928658,0.051955562084913254,0.04075280948066526,2.8075579940341413,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",500,23.588395631639287,3.3302667140960693,2.661783218383789,0.8410052061080933,0.04812021734430938,0.7820965722203255,0.18311790291536278,0.04917173087596893,0.04619846406606563,2.972090410068631,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",0,0.001479852944612503,7.084741592407227,7.131319522857666,0.9659129157662392,0.010738190049675024,1.9995705857872963,0.2862232589727352,0.2567539419978857,0.14358109554229917,1.174997844034806,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",10,0.47766825393773615,6.282107353210449,6.316427230834961,0.952546663582325,0.01523654536757299,1.4264571443200111,0.17731517564433613,0.13003714568912983,0.0851285470193035,1.9863012351561338,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",20,0.9543516987469047,6.143274784088135,6.154204368591309,0.9499513171613216,0.01613796820495975,1.3992606103420258,0.16919457142690694,0.12390411086380482,0.08597515546866578,2.158215519040823,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",30,1.4297448906581849,6.031466484069824,5.980973243713379,0.9484252072870731,0.017085444773061353,1.4031121879816055,0.19080685461985136,0.12221746519207954,0.07838445034194547,2.09011938306503,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",40,1.9052437106147408,5.895740985870361,5.863394260406494,0.9476947784423828,0.0170473827979816,1.3478417173027992,0.17421614762396764,0.10965900495648384,0.07992166653730068,1.8991028601303697,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",50,2.3776190779171884,5.81205415725708,5.770228385925293,0.9467474892735481,0.017823904153577357,1.2920836806297302,0.1606414775644286,0.10030526295304298,0.07923738800864798,1.8988019090611488,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",60,2.8504097850527614,5.672096252441406,5.663071632385254,0.9474487118422985,0.015869564536311772,1.297444425523281,0.12697446051640607,0.09939371421933174,0.07635769470928418,2.113589577144012,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",70,3.3260877809952945,5.5283122062683105,5.522197723388672,0.9458327069878578,0.017807337377315668,1.2682867720723152,0.11536843649859621,0.09786427766084671,0.07425694975038087,2.170525649096817,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",80,3.7984999218024313,5.395784378051758,5.385618686676025,0.9432072527706623,0.017482781399347197,1.230934053659439,0.1261210771925181,0.09285346046090126,0.06919906948410927,2.194469649111852,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",90,4.268587414873764,5.238387584686279,5.237710475921631,0.9378345496952534,0.020265197118288957,1.2036030441522598,0.16095397106357678,0.08707655221223831,0.06572039862583556,2.1974612469784915,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",100,4.742455370957032,5.128602027893066,5.117464065551758,0.9337473176419735,0.02018612280551354,1.1676759235560894,0.12382717618667764,0.0863446556031704,0.06833162951371095,2.187261196086183,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",110,5.214960625628009,4.962674140930176,4.944568634033203,0.9270307570695877,0.025347774632339094,1.1163655705749989,0.11293694254530552,0.07753703743219376,0.062166931509236405,2.3076240269001573,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",120,5.6908618290908635,4.881495475769043,4.76167631149292,0.9249261319637299,0.0239982891291948,1.0953341200947762,0.1266355104745979,0.07840711995959282,0.0654571952560391,2.203141844132915,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",130,6.1645633827429265,4.720099449157715,4.626091003417969,0.916412279009819,0.0285950491430548,1.0673150941729546,0.10618456898842119,0.07648763805627823,0.0637895519758638,2.3120841269847006,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",140,6.638337391428649,4.58098030090332,4.446678638458252,0.9106108024716377,0.03127643894675803,1.0365264527499676,0.1383374929460511,0.0776595026254654,0.06800135622736916,2.37558309501037,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",150,7.118277921108529,4.500391006469727,4.362310886383057,0.9078056253492832,0.0320820332930153,0.997056383639574,0.09558522153776546,0.0688457190990448,0.0648896228756602,2.4057598609942943,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",160,7.594610127154738,4.504881858825684,4.264740467071533,0.9064355380833149,0.0337357571902542,0.9785061664879322,0.1350794275078536,0.06954292207956314,0.06557356908998763,2.3261675930116326,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",170,8.070858270628378,4.388359069824219,4.1437835693359375,0.8936461582779884,0.038454259488950614,0.9541995488107204,0.15318962058858074,0.06876473128795624,0.06061903416523962,2.2233666030224413,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",180,8.54529540380463,4.256736755371094,4.0469441413879395,0.8938305750489235,0.0337419434578044,0.9262151792645454,0.13723616575454212,0.061932049691677094,0.05551249828530836,2.424092784989625,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",190,9.021952473558486,4.268844127655029,4.009680271148682,0.888549480587244,0.04049116305886368,0.9154454171657562,0.1211769177795955,0.06323271244764328,0.061945166210792615,2.394192541949451,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",200,9.505282938480377,4.195351600646973,3.922010660171509,0.8892834261059761,0.03454355323181632,0.912859033793211,0.11712888598771143,0.06381481513381004,0.06105442325840237,2.385991984978318,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",210,9.983812239486724,4.1020283699035645,3.817021369934082,0.8843390308320522,0.0394077480702038,0.8890572302043438,0.12449822516881254,0.05795161426067352,0.054369601702087794,2.2390141510404646,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",220,10.461387490388006,4.086636543273926,3.7889621257781982,0.885620478540659,0.03558692826915473,0.8905063085258007,0.11634535739179358,0.059226591140031815,0.05250240402375619,2.4581649811007082,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",230,10.939157268963754,4.001543045043945,3.6903061866760254,0.879639595746994,0.03919578889885361,0.883644737303257,0.1386708158008636,0.05933665856719017,0.05675084989794639,2.4499318769667298,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",240,11.41190741606988,3.928619384765625,3.5742688179016113,0.8714902549982071,0.039894001082295995,0.8460759781301022,0.0947839404217179,0.05269403010606766,0.047231096375266166,2.492493118159473,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",250,11.884178694570437,3.9160211086273193,3.501924991607666,0.8684983439743519,0.041783844728577296,0.8646699711680412,0.1258422722135953,0.05567030608654022,0.05118035607192782,2.563501713098958,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",260,12.360987889114767,3.8739495277404785,3.483023166656494,0.8719829805195332,0.04272488473456948,0.8714501298964024,0.1302314066704145,0.05867974832653999,0.055414164515944025,2.355196757009253,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",270,12.836874031228945,3.847918748855591,3.4189906120300293,0.8695347048342228,0.03804080590724333,0.8476432450115681,0.13070875014403197,0.05633100867271423,0.05105740766067855,2.5531358290463686,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",280,13.31105236406438,3.7599289417266846,3.321547031402588,0.8658735677599907,0.029772592945827337,0.8497056812047958,0.1158976367933328,0.0557590015232563,0.05993612219775885,2.6027273719664663,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",290,13.78748870594427,3.7107443809509277,3.252034902572632,0.862733468413353,0.03644089621179306,0.8346029594540596,0.10597500973626854,0.05222858861088753,0.04723514185854976,2.671732547925785,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",300,14.254960952326655,3.6183485984802246,3.1938769817352295,0.8615720979869366,0.041106055387943374,0.8349227719008923,0.1342695701225802,0.0547245629131794,0.04895488900608154,2.627440013922751,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",310,14.73268748098053,3.5970587730407715,3.1610074043273926,0.8703642189502716,0.03835915052529962,0.8085796013474464,0.09886600575565266,0.048043910413980484,0.04698651723468168,2.7196947811171412,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",320,15.213140738196671,3.564547538757324,3.0657901763916016,0.8516543172299862,0.040629738868064244,0.816764697432518,0.09588094118530596,0.0508551187813282,0.04531341191291919,2.4470336358062923,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",330,15.69119334872812,3.5130977630615234,3.0352187156677246,0.8604394309222698,0.0414831872390653,0.8197734244167805,0.1311938715361978,0.051400236785411835,0.05455924721281977,2.762141047976911,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",340,16.172493050107732,3.5106124877929688,3.0032544136047363,0.8569350428879261,0.04045664695317819,0.8338825069367886,0.08540660362316388,0.051537856459617615,0.0446405944433452,2.6764393160119653,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",350,16.642932135844603,3.4933395385742188,2.943246603012085,0.8541901744902134,0.045407346913773704,0.8167506344616413,0.10327683480410411,0.04939259961247444,0.04515413222958165,2.7129792131017894,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",360,17.109628041973338,3.390673875808716,2.8857293128967285,0.8518075048923492,0.03962857795375592,0.8391560576856136,0.1341187830319508,0.05246153101325035,0.04590087152645003,2.7213111771270633,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",370,17.77521206322126,3.3986549377441406,2.876007318496704,0.853730246424675,0.045738258775618754,0.830893911421299,0.12691733496027263,0.04961637035012245,0.040604798625164445,3.683790696784854,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",380,18.248042677994817,3.3179421424865723,2.808411121368408,0.845123365521431,0.04272554950737815,0.8003933876752853,0.09977973374652688,0.04330869019031525,0.03204566312810596,2.6986100908834487,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",390,18.724551459308714,3.3262293338775635,2.773232936859131,0.8513824641704559,0.04849163161209537,0.7967059724032879,0.1465393081287305,0.04808438569307327,0.04867240922298534,2.703405298991129,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",400,19.1996443036478,3.28747820854187,2.739323616027832,0.8431409820914268,0.045206928646202266,0.7946808263659477,0.12392583704415938,0.04598057270050049,0.04305693674964611,2.806803261162713,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",410,19.67657611356117,3.3204901218414307,2.7628731727600098,0.8515005335211754,0.04905137221918004,0.812406912446022,0.1555719687141337,0.050540514290332794,0.04634443280962186,2.723393850028515,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",420,20.159278323408216,3.2678375244140625,2.6695985794067383,0.8536069430410862,0.04521463088711274,0.8188862316310406,0.12658064249055004,0.045362938195466995,0.039697549535995254,2.7235481499228626,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",430,20.63761655590497,3.239985942840576,2.683417558670044,0.8561071790754795,0.032728872047943475,0.8344813473522663,0.1340788988825107,0.04944664239883423,0.04477844890145576,2.541531963972375,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",440,21.11719948379323,3.2333579063415527,2.609591484069824,0.8441142924129963,0.03959396238256872,0.7715474553406239,0.11123709818851893,0.04309528321027756,0.04764784604455033,2.7754853321239352,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",450,21.5912823388353,3.199427366256714,2.6109776496887207,0.8417958766222,0.04875491843201573,0.8154560476541519,0.14384150875012297,0.048674385994672775,0.040607200025930476,2.7233667878899723,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",460,22.070214172126725,3.183849811553955,2.5609140396118164,0.83349334821105,0.052894117178461424,0.7715100683271885,0.1407915490357779,0.04400799795985222,0.04577089344412085,2.8174389200285077,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",470,22.543631229316816,3.1880195140838623,2.6105568408966064,0.8553473651409149,0.045468344712733685,0.8187003135681152,0.10274130210731322,0.044877249747514725,0.03645196213398587,2.7290631111245602,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",480,23.029992633964866,3.1922528743743896,2.524168014526367,0.8359382674098015,0.048744025775697225,0.7906939536333084,0.11139390187543766,0.04400467127561569,0.033612921397190576,2.4622670349199325,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",490,23.520651733037084,3.2282819747924805,2.4945414066314697,0.841549951583147,0.041109349493770736,0.785590872168541,0.11892764610862112,0.0402015745639801,0.028172611849407794,2.8208930960390717,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",0,0.0015638580080121756,7.047137260437012,7.080456733703613,0.9629532136023045,0.010440190895203571,1.968964770436287,0.26736812169855934,0.24368837103247643,0.13819121189620154,1.7221289142034948,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",10,0.4626884013414383,6.292803764343262,6.307116508483887,0.9536132141947746,0.015857451663457263,1.4831697046756744,0.19788934500853347,0.14428678527474403,0.08835225534809711,1.6225154078565538,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",20,0.9377282224595547,6.162863254547119,6.1355719566345215,0.9515385515987873,0.014649301729368238,1.4130458608269691,0.1577650032382044,0.11939748376607895,0.08601875481238525,1.968099064892158,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",30,1.3962609900627285,5.941013813018799,5.941073417663574,0.947602566331625,0.013640657685477233,1.3495956882834435,0.14010928760079946,0.10725798271596432,0.07928114913697372,1.982786966022104,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",40,1.8552566824946553,5.834902763366699,5.8557329177856445,0.9471737295389175,0.01606474927154225,1.3537153974175453,0.13519868395360848,0.11304997280240059,0.08019871462866383,2.024697595043108,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",50,2.3185218046419322,5.706528186798096,5.732851982116699,0.9444603100419044,0.017750348015449828,1.3060669749975204,0.1405881802239146,0.10892516747117043,0.08286854878797881,2.079460249049589,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",60,2.7762998768594116,5.594488143920898,5.566369533538818,0.9403669759631157,0.021194655699267262,1.2424306347966194,0.13265173853472786,0.0944976881146431,0.07264294127520945,2.136217681923881,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",70,3.237229563528672,5.465839862823486,5.460009574890137,0.9440619572997093,0.01712694409756582,1.2675479054450989,0.12276221113678987,0.09874791279435158,0.07594218564708365,2.1245110048912466,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",80,3.6988768505398184,5.325874328613281,5.287845611572266,0.9380197077989578,0.01637990780720785,1.2083027735352516,0.13613033017335985,0.09077892825007439,0.0723898120780028,2.152976287063211,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",90,4.1600350111257285,5.215791702270508,5.1769490242004395,0.9324333630502224,0.01847259275028993,1.1599652469158173,0.1485553069697456,0.08672970160841942,0.07035405996140107,2.238399662077427,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",100,4.623543893918395,5.046514987945557,5.002065181732178,0.92755576223135,0.01893861072427285,1.1475105285644531,0.14038567587122297,0.08637331798672676,0.06848288694989062,2.3127266159281135,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",110,5.08537289686501,4.884946823120117,4.843659400939941,0.9268822707235813,0.023284381890836998,1.1305412575602531,0.13037394006180758,0.08480444923043251,0.06922629202213562,2.3317571450024843,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",120,5.548202522564679,4.702099800109863,4.650423049926758,0.9207138940691948,0.02267630948954828,1.1033206805586815,0.13851841064257728,0.08145864307880402,0.06175473474402966,2.341904489090666,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",130,6.0106882601976395,4.637815952301025,4.556059837341309,0.911405935883522,0.02756847431836546,1.0600706785917282,0.12607582984955404,0.07749928906559944,0.06359526711650783,2.4248259379528463,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",140,6.474915636936203,4.573763847351074,4.4138407707214355,0.9053343273699284,0.030907836690690492,1.0260089188814163,0.14992299325999303,0.07204509153962135,0.05895612437743841,2.4791374548804015,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",150,6.938590072561055,4.423442840576172,4.2579569816589355,0.9018844962120056,0.033382321321202946,1.012425146996975,0.13480804008609215,0.07367486879229546,0.06428904597123096,2.1618395729456097,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",160,7.39981379150413,4.334066867828369,4.137781143188477,0.896848127245903,0.03613428837874546,0.9672607593238354,0.1445705559742614,0.06754761561751366,0.05792017822376463,2.2436043608468026,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",170,7.864144963212311,4.312274932861328,4.1036696434021,0.8934018090367317,0.03156397271253158,0.9469338655471802,0.12565940063739608,0.0651700459420681,0.057401428169607104,2.4189246350433677,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",180,8.325492526171729,4.267549514770508,3.976926326751709,0.8894648589193821,0.04049913717265321,0.935411736369133,0.16879218275560315,0.06635483726859093,0.0554072671392139,2.198670153040439,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",190,8.785404405323789,4.261401176452637,3.9238648414611816,0.8912072815001011,0.03657378080667025,0.94752037525177,0.16633895911691132,0.0692916102707386,0.0561803949383943,2.442822540877387,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",200,9.240790086099878,4.191598892211914,3.7873311042785645,0.8810425512492657,0.040252520349527234,0.9175375737249851,0.13181118058549368,0.06605998426675797,0.061605754896139016,2.3476052980404347,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",210,9.699131790548563,4.144647121429443,3.7392706871032715,0.8811329677700996,0.04236114159661558,0.8738507814705372,0.15421635952755064,0.05382194370031357,0.04559057219766764,2.3788392280694097,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",220,10.162665012292564,4.038394451141357,3.652308940887451,0.8794855922460556,0.03679589002276334,0.9240683130919933,0.17258521432258697,0.06247452273964882,0.05034843839071564,2.3882734491489828,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",230,10.6259672222659,4.04615592956543,3.6128997802734375,0.8702990636229515,0.03867416212702067,0.8894996084272861,0.19409999319602964,0.062382642179727554,0.05477860448689454,2.362023194786161,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",240,11.087121420074254,3.994537591934204,3.5357706546783447,0.8742067292332649,0.04014234531972968,0.8687012828886509,0.16576003854253174,0.05812585726380348,0.05048559832057139,2.4584047878161073,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",250,11.54739289265126,4.008117198944092,3.514303207397461,0.874725166708231,0.03914094739859108,0.8927334547042847,0.19141265280233644,0.06478630378842354,0.05429810585205695,2.4437234150245786,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",260,12.008892053039744,3.9536027908325195,3.422858953475952,0.8714883141219616,0.045485729324979435,0.8872281834483147,0.16788868759961476,0.06631126627326012,0.0648074569883977,2.2813271558843553,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",270,12.469013268360868,3.869250774383545,3.3891754150390625,0.8695987425744534,0.04503245075494579,0.8733534626662731,0.2128621425358921,0.05838453769683838,0.050407741254166076,2.5147528320085257,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",280,12.93037246959284,3.848841905593872,3.306058883666992,0.8722239695489407,0.04315767140604996,0.8582121059298515,0.1460133081746741,0.056235555559396744,0.047149165585107095,2.445586347952485,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",290,13.497501442441717,3.7692580223083496,3.2716856002807617,0.8674309030175209,0.04676624400498213,0.8272969238460064,0.16202333785332565,0.050255537033081055,0.04438718255024064,2.5875878820661455,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",300,13.960050579393283,3.8023805618286133,3.229027271270752,0.8631859384477139,0.0524786715642116,0.869980726391077,0.19838881935068817,0.06345432996749878,0.05954697978846749,2.4991921030450612,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",310,14.422910733846948,3.761404037475586,3.190345048904419,0.8631609790027142,0.05204330361560898,0.8385170325636864,0.20891505003748947,0.05466591939330101,0.04926953898319049,2.46720103896223,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",320,14.883790318621323,3.7352890968322754,3.110442638397217,0.8587937578558922,0.053329291184633226,0.8371356800198555,0.17753999732823583,0.05397060886025429,0.044791911129703536,2.5207947860471904,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",330,15.34964662254788,3.6912083625793457,3.07507061958313,0.8572396226227283,0.059191471997197515,0.8391952626407146,0.19218608003553891,0.05894112586975098,0.052857569396274895,2.52805987582542,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",340,15.81068368232809,3.6268715858459473,2.9815673828125,0.8536979667842388,0.057185715502800286,0.7903916053473949,0.16002734377558273,0.0472964309155941,0.04188809727396914,2.605754599906504,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",350,16.275398711208254,3.643270492553711,2.984498977661133,0.8534864895045757,0.05171579694108766,0.8123988285660744,0.17085935897150284,0.05254625156521797,0.04835891675201678,2.5426815100945532,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",360,16.744607007596642,3.6180293560028076,2.9388346672058105,0.8548571765422821,0.05519925773503013,0.8193912617862225,0.23111599271672947,0.049300212413072586,0.03560079559264461,2.7123119628522545,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",370,17.21499682031572,3.628541946411133,2.887183666229248,0.8534199260175228,0.0561312792043694,0.8016178235411644,0.18994241184591093,0.048704519867897034,0.04220810531099254,2.627453476889059,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",380,17.680094816954806,3.572826862335205,2.8556177616119385,0.8543426804244518,0.05312709941415799,0.7943829521536827,0.16583586705563727,0.04895523190498352,0.04385216921608185,2.6420308500528336,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",390,18.150015393737704,3.641369342803955,2.8008406162261963,0.8440546467900276,0.06321574924904935,0.7591235265135765,0.15201782252540985,0.03888290002942085,0.029830338054969582,2.4674066859297454,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",400,18.61180939222686,3.44472074508667,2.796347141265869,0.8527207113802433,0.05802662871591206,0.8058122545480728,0.1832224208637441,0.052859097719192505,0.05363064167469695,2.7361040369141847,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",410,19.072505675256252,3.494924783706665,2.757138967514038,0.8459488525986671,0.06326777591692015,0.7794530875980854,0.1742391184258543,0.04676420986652374,0.037922084100208205,2.410123982001096,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",420,19.533229734748602,3.487485885620117,2.7369272708892822,0.853790033608675,0.05722437899747138,0.7746529839932919,0.1656871694062577,0.04367793723940849,0.04829419984449083,2.6040093989577144,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",430,19.992856261786073,3.4212851524353027,2.744727611541748,0.8525412976741791,0.05759784688449582,0.8006463423371315,0.18678676811558506,0.04585497826337814,0.039492799362268266,2.6918635200709105,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",440,20.450560461962596,3.4701006412506104,2.671229600906372,0.8488470502197742,0.062434163567996276,0.7906077727675438,0.2274544686521567,0.04774346947669983,0.0453040945057169,2.763310550013557,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",450,20.91444227914326,3.390371322631836,2.6124258041381836,0.8515361621975899,0.06623752270122862,0.7904101721942425,0.27801495333063875,0.049364522099494934,0.04654137924776555,2.7952209119684994,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",460,21.380084364674985,3.379584312438965,2.5644781589508057,0.8478660061955452,0.054929895946639655,0.7664284892380238,0.20902153849292585,0.04379066824913025,0.03643280897048545,2.789679838111624,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",470,21.84302723244764,3.362360954284668,2.5821940898895264,0.8605940267443657,0.049550427245095584,0.8060999251902103,0.18197277933908831,0.054480768740177155,0.05875130087975264,2.841400950914249,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",480,22.31144937337376,3.4156155586242676,2.59240460395813,0.8431706577539444,0.06411061471608541,0.761780709028244,0.1935334409249703,0.04010573774576187,0.02994451563928863,2.859797441167757,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",490,22.780569709138945,3.216930866241455,2.519656181335449,0.8507721088826656,0.054216288119880045,0.7466428875923157,0.1870382232070113,0.04243146628141403,0.042961812545852796,2.939658819930628,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",500,23.245655252132565,3.333122730255127,2.49177885055542,0.8486023284494877,0.05783436220519205,0.7532292269170284,0.19618686757852793,0.041177500039339066,0.03555769632568611,2.948738881153986,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",510,23.722054813057184,3.2151942253112793,2.4902453422546387,0.8460708037018776,0.057727665162367735,0.7699369769543409,0.21591281179433774,0.04501036927103996,0.035227336778443016,2.614620503038168,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",520,24.196092534577474,3.272547960281372,2.4991514682769775,0.8627280220389366,0.0572786165782114,0.8517433591187,0.25215651362767577,0.05924348533153534,0.04720537140432445,2.6616297310683876,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",530,24.671915378421545,3.2224464416503906,2.4557437896728516,0.8444207310676575,0.056569182064056256,0.7964723277837038,0.22833008520879117,0.050967950373888016,0.04181188613630963,2.643549870001152,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",540,25.145675769541413,3.1563363075256348,2.422252893447876,0.861327525228262,0.05876658776318953,0.8090692274272442,0.23976672780009817,0.048688653856515884,0.033933615272294895,2.7203942260239273,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",550,25.62292656605132,3.1150662899017334,2.391517400741577,0.8527254909276962,0.06027803247328081,0.765051294118166,0.25312375833068146,0.04033667594194412,0.02924876171922876,2.714007538044825,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",560,26.09733054903336,3.055689811706543,2.3905420303344727,0.8543071299791336,0.04741106434865024,0.7873110324144363,0.23755229561588734,0.04617763310670853,0.032320894823653766,2.594842280028388,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",570,26.571161778876558,3.123979330062866,2.3534433841705322,0.8612061738967896,0.058254313461007484,0.8300397843122482,0.262470600173237,0.05294549837708473,0.04111964546219223,2.659236318897456,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",580,27.028805643087253,3.2621548175811768,2.3974967002868652,0.851500391960144,0.05471149055978825,0.790866918861866,0.25872474861461536,0.04653479531407356,0.03653045943013024,2.5720019680447876,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",590,27.504324244102463,3.1678175926208496,2.3849663734436035,0.8585526831448078,0.05662455349375338,0.7863184474408627,0.2613765809370994,0.046048104763031006,0.03939344626226384,2.619813933968544,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",0,0.002130666980519891,7.181727409362793,7.18787956237793,0.9673790894448757,0.012295728538713803,2.0385433360934258,0.2785299450031331,0.26094318740069866,0.14718447427901957,0.938339106971398,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",10,0.48035346320830286,6.327408790588379,6.344206809997559,0.9520577415823936,0.016491749457498737,1.4459517821669579,0.2127799752997647,0.1330045908689499,0.07796934435789303,1.7207744538318366,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",20,0.9446548721753061,6.192333221435547,6.175089359283447,0.95282082259655,0.014920082948436067,1.4391415193676949,0.14401569863944802,0.13020505383610725,0.09433663224433783,1.780380048090592,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",30,1.4181633959524333,5.986352920532227,6.004952430725098,0.9510210007429123,0.014445662421703549,1.4358952715992928,0.12946686281156908,0.12504149787127972,0.08579054688474728,1.788488443940878,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",40,1.8907249548938125,5.866488456726074,5.875201225280762,0.9504708759486675,0.014259668468803154,1.3767762407660484,0.15113578628383026,0.11222971230745316,0.07863778503452298,1.8399026901461184,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",50,2.3669545156881213,5.7193603515625,5.745630741119385,0.9485505968332291,0.013989011644567704,1.3054096400737762,0.12321011026318013,0.1003199964761734,0.07294152797358315,1.8646561969071627,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",60,2.8417776359710842,5.613724231719971,5.624853134155273,0.9472369328141212,0.01737160105670115,1.2778385058045387,0.12664872755143422,0.09776430204510689,0.07355400657079145,1.825986881973222,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",70,3.3139013131149113,5.497810363769531,5.495998382568359,0.9440917000174522,0.015620705439934237,1.2681947126984596,0.11351908989890228,0.09621331095695496,0.07643248114516772,1.8120565488934517,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",80,3.7813936739694327,5.400079727172852,5.392298698425293,0.9420148767530918,0.015838455577382674,1.218373954296112,0.12348617002327765,0.08725026622414589,0.06705162983433055,2.0224073259159923,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",90,4.252005051355809,5.25007963180542,5.222607135772705,0.9385685659945011,0.016663552166653574,1.1884384229779243,0.09938183001273809,0.08672226965427399,0.06986244753221556,2.0467464530374855,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",100,4.717165311332792,5.106059551239014,5.043511390686035,0.9316997937858105,0.01915623740328095,1.1526767946779728,0.09876307827164911,0.08271751552820206,0.06778370263417552,1.805908580077812,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",110,5.188535197405145,4.982636451721191,4.881885051727295,0.9289253316819668,0.018856714638827887,1.125733494758606,0.08314534788999395,0.07956721261143684,0.06671175686835204,2.0940256039611995,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",120,5.6573984085116535,4.824893951416016,4.714732646942139,0.9199845008552074,0.022969354340665504,1.0704970583319664,0.09944530246618187,0.07667191699147224,0.06773347350683974,2.1066698590293527,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",130,6.134152912301943,4.6656365394592285,4.545704364776611,0.9173266850411892,0.02265448570726192,1.0844344645738602,0.10136893872294588,0.08056120574474335,0.06558123074249539,1.9567041390109807,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",140,6.611237213481218,4.537700653076172,4.389840126037598,0.9080201648175716,0.027133937308882475,1.011031273752451,0.09716185786760267,0.07145096361637115,0.06228348661929923,2.262510766973719,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",150,7.089288791175932,4.449070453643799,4.2649455070495605,0.8978608585894108,0.03264018858158472,0.9665050469338894,0.1307680904745258,0.06594165787100792,0.06341973880013359,2.044182697078213,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",160,7.561259398702532,4.353283882141113,4.16470193862915,0.8967881128191948,0.03304912501035766,0.973501905798912,0.1170895489470498,0.0687708929181099,0.06189344560971495,2.2638970979023725,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",170,8.035580241121352,4.314082622528076,4.135128974914551,0.9014575183391571,0.034562672333825525,0.9844770506024361,0.14316454066207351,0.07361070439219475,0.06782296765922095,2.32031281106174,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",180,8.504915841389447,4.25270414352417,3.995436906814575,0.8877237737178802,0.0406533237226488,0.9429779574275017,0.1470940475058346,0.06750337034463882,0.063775389826844,2.0834723110310733,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",190,8.97881578700617,4.1370930671691895,3.9053807258605957,0.8952674567699432,0.0350484873296093,0.9426261745393276,0.16291352049702598,0.06732931733131409,0.06521621204034465,2.360116903902963,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",200,9.442466071574017,4.111661434173584,3.851365804672241,0.8838167116045952,0.03834436217315554,0.9056623131036758,0.14176503611368893,0.06272225081920624,0.061294901600034285,2.32544320798479,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",210,9.909486652351916,3.929598093032837,3.730048656463623,0.878722045570612,0.04545152769874699,0.9065281711518764,0.1757789683400532,0.06307144463062286,0.05920941629863054,2.1857162739615887,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",220,10.380588431376964,4.000249862670898,3.664271593093872,0.8773236311972141,0.044449068997877164,0.9104767441749573,0.18063283881339845,0.06118716299533844,0.059211210416106025,2.388810701901093,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",230,10.850096946349367,3.9045166969299316,3.621539354324341,0.8743605688214302,0.04786503745875859,0.8810529038310051,0.17902864348432,0.0601164773106575,0.05983091326686744,2.304701798129827,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",240,11.309491450199857,3.824725389480591,3.495851993560791,0.8714606575667858,0.046919548869258955,0.883009783923626,0.1598834381579608,0.060382019728422165,0.058468085099946825,2.1152201648801565,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",250,11.780771346297115,3.7507758140563965,3.4380602836608887,0.8677986152470112,0.04369424342190791,0.8675024323165417,0.17209142733600016,0.05933041498064995,0.052475097215740354,2.4390426189638674,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",260,12.251655275234953,3.725400686264038,3.3656206130981445,0.8702424913644791,0.05256221371524597,0.8789726309478283,0.18774075754904818,0.05883822590112686,0.05328555567061979,2.508356509031728,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",270,12.726090217242017,3.6488659381866455,3.2822296619415283,0.8658447898924351,0.04960118044750849,0.8671862483024597,0.17440472553712902,0.05963882431387901,0.06244145962338884,2.655010593822226,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",280,13.191402749158442,3.5762994289398193,3.2227067947387695,0.8666361272335052,0.0460261590396632,0.8762584701180458,0.15412083779801283,0.05691244453191757,0.049442746371007436,2.5214098100550473,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",290,13.667716265888885,3.570803165435791,3.1920971870422363,0.8649928458034992,0.04469486338523012,0.8700211346149445,0.15452715420256935,0.05942633002996445,0.05856840021378979,2.6439581918530166,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",300,14.148243094794452,3.561795234680176,3.1340832710266113,0.8594354912638664,0.046727943776027674,0.8495960757136345,0.16249734973432656,0.05556529760360718,0.05037203148345441,2.6443919520825148,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",310,14.628753463039175,3.5883426666259766,3.154533624649048,0.8599821031093597,0.04533021256388059,0.871794518083334,0.2053254905436006,0.05725075304508209,0.05277154137288386,2.6629150537773967,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",320,15.086724085500464,3.3732080459594727,2.9833319187164307,0.8594380989670753,0.04430130770700972,0.8402427658438683,0.1684177201597888,0.05453484505414963,0.048763606254491026,2.4844215251505375,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",330,15.558591541368514,3.415295362472534,3.018342971801758,0.8591617047786713,0.04641235618798759,0.8819202780723572,0.1801341211911368,0.06236827373504639,0.051204664164255884,2.639002500101924,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",340,16.03595808474347,3.360726833343506,2.9241178035736084,0.8587950952351093,0.04884477469158706,0.8703520074486732,0.23993295292702715,0.059134963899850845,0.048099560422538164,2.7210500170476735,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",350,16.495984219713137,3.3118839263916016,2.8556039333343506,0.8551642820239067,0.04880910053843153,0.835853211581707,0.14538488904141209,0.05146018788218498,0.04296022754638789,2.5869379150681198,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",360,16.953893857542425,3.382089138031006,2.852602243423462,0.864197626709938,0.0435029794566048,0.8841284438967705,0.18803451368441412,0.05830928683280945,0.04251334447338427,2.5567698639351875,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",370,17.428518847329542,3.287046194076538,2.80094051361084,0.8590751551091671,0.04437597362060999,0.8526227958500385,0.16929336072666165,0.05635957047343254,0.04714188737786164,2.8556230592075735,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",380,17.89876843430102,3.2342348098754883,2.7814295291900635,0.8543376959860325,0.04086849536670469,0.8359367288649082,0.15933337990804658,0.05241381376981735,0.052657160742868074,2.902000362984836,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",390,18.36296934983693,3.193706512451172,2.7233657836914062,0.8549508787691593,0.05083396149126531,0.8240060806274414,0.18576725687534504,0.054479729384183884,0.049427469944528624,2.6071096730884165,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",400,18.825997145380825,3.1678061485290527,2.714494466781616,0.8548917658627033,0.051279164627027224,0.8361509963870049,0.20839633234134328,0.050306860357522964,0.03680152767498608,2.7246876240242273,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",410,19.30001781368628,3.223691463470459,2.7257144451141357,0.8525699973106384,0.05280725474914728,0.7911245934665203,0.14963692213879978,0.047230709344148636,0.04075612282827971,2.9265202728565782,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",420,19.77332368423231,3.1925430297851562,2.690110683441162,0.8522668294608593,0.04863564765549535,0.8027939945459366,0.14824996834480364,0.052276860922575,0.05214575498046926,2.860867909854278,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",430,20.230783607345074,3.153142213821411,2.6288514137268066,0.8510769121348858,0.05171517798724019,0.8237478397786617,0.17340978621534836,0.05205325409770012,0.03773149635211715,2.892116033937782,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",440,20.69465150195174,3.142214298248291,2.6447694301605225,0.8440236449241638,0.04740740335576769,0.8241098299622536,0.14372513253983593,0.04900745674967766,0.03651964212227254,2.7484018860850483,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",450,21.169976009055972,3.1185669898986816,2.588974714279175,0.8448183462023735,0.05006200387788288,0.7543693631887436,0.16911129047704207,0.04371275007724762,0.04956826487140065,2.924581423169002,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",460,21.642091351328418,3.1145811080932617,2.5828309059143066,0.8531207852065563,0.061570815909751934,0.8096662573516369,0.16554750071620472,0.04818171635270119,0.03710205136805496,2.958271797047928,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",470,22.10641852230765,3.1305017471313477,2.5438008308410645,0.8429910838603973,0.048788647746517214,0.8396239914000034,0.1483179376483921,0.05667741596698761,0.04281257609832783,2.710049106972292,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",480,22.580626282142475,3.059174060821533,2.520111322402954,0.8395982943475246,0.04799882699435974,0.8212824650108814,0.2176896903920105,0.04795369878411293,0.031682136092060206,2.9844040039461106,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",490,23.048479542136192,3.0237138271331787,2.481919050216675,0.8430810607969761,0.057844267162091546,0.8183541335165501,0.2115991328496349,0.04938539117574692,0.03188360738809335,2.9184545010793954,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",500,23.637939372332767,3.028263807296753,2.467503309249878,0.8477181047201157,0.04570489144493404,0.8170151673257351,0.18809715723673248,0.0500776469707489,0.041453445966923796,4.202103519113734,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",510,24.221599530195817,3.089138984680176,2.4860429763793945,0.8483557440340519,0.05123953304099904,0.8090185560286045,0.17749800902973756,0.045143481343984604,0.02676099246986647,3.8653908527921885,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",520,24.818251290591434,3.0065481662750244,2.3966047763824463,0.8435967452824116,0.05045437857162809,0.8137519210577011,0.23712905456476585,0.048092857003211975,0.03367221054202451,4.086129364091903,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",530,25.41211158852093,3.0384442806243896,2.3860435485839844,0.8334057591855526,0.05618962342879382,0.7649411298334599,0.15461827403551723,0.04380074888467789,0.03485186686320418,4.399162024958059,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",540,26.00625120801851,3.093700885772705,2.408189058303833,0.8464409820735455,0.047581515386658044,0.8127012588083744,0.17136477620374327,0.04995126277208328,0.0389467631217092,4.107622059993446,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",550,26.478396310005337,3.0428740978240967,2.367814540863037,0.8433561250567436,0.05314422670848694,0.8184079974889755,0.22238296232115826,0.0516015887260437,0.03693375175733713,3.0761637839023024,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",0,0.001901783049106598,7.116262912750244,7.136990070343018,0.9661861881613731,0.012357979701403201,2.0267049968242645,0.28669147846559934,0.26194062270224094,0.1453912494703135,0.9572836940642446,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",10,0.4750967468135059,6.295516490936279,6.3270463943481445,0.9507469832897186,0.01558196495569863,1.4332007244229317,0.19336983225310325,0.1319224163889885,0.08538861925578163,1.7223589231725782,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",20,0.9455819628201425,6.181318283081055,6.157199382781982,0.9511742629110813,0.015218939635079854,1.4483243450522423,0.17083987707696732,0.1320474799722433,0.09448446322830374,2.0425008749589324,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",30,1.414668555604294,5.983114242553711,5.975255012512207,0.9513562172651291,0.015364773139467934,1.381625458598137,0.1524690935745894,0.11677278950810432,0.07886710189401203,1.8611195969861,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",40,1.8792739263735712,5.867656230926514,5.847649574279785,0.9481523111462593,0.01612992677675943,1.3535910546779633,0.15155867224230624,0.11057143285870552,0.08383315536020643,1.7422059099189937,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",50,2.3553069941699505,5.731863498687744,5.709251403808594,0.9477257840335369,0.01770238835177619,1.3257288113236427,0.1627013304944888,0.10442396998405457,0.07699161456015537,1.8873455938883126,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",60,2.8319384732749313,5.619270324707031,5.585960388183594,0.9447635486721992,0.020583658257375483,1.2767227366566658,0.1520197096058107,0.0976128950715065,0.07869642174284822,1.9556592849548906,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",70,3.2893813082482666,5.517436504364014,5.474935531616211,0.943696703761816,0.015583466329120483,1.2713574692606926,0.14696778963235935,0.09737881645560265,0.07932202403430628,1.8417932300362736,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",80,3.7484176545403898,5.399326324462891,5.334413528442383,0.937225092202425,0.01865021408794872,1.1922106817364693,0.1541633354233757,0.08364185318350792,0.06470573966538916,1.8690737870056182,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",90,4.219651496969163,5.267391681671143,5.163875579833984,0.9341765306890011,0.02071015194135069,1.174604807049036,0.14479157101443554,0.08217494562268257,0.06796461542330494,2.0259710501413792,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",100,4.691930233966559,5.167553901672363,5.023712158203125,0.9318204745650291,0.019907143782058136,1.182953093200922,0.16182752123551022,0.09221469983458519,0.07583482657906158,2.0791458408348262,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",110,5.152208224404603,4.965996742248535,4.822829723358154,0.927550695836544,0.02258405480309145,1.140322733670473,0.1790827261353593,0.08640456572175026,0.07486528423720792,1.9754318008199334,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",120,5.611237111501396,4.879129409790039,4.695986270904541,0.9228233620524406,0.025146205856315582,1.0865170322358608,0.155092253852392,0.07757402583956718,0.07121375093379034,1.9802399040199816,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",130,6.093378135934472,4.735793113708496,4.543426036834717,0.914736196398735,0.0287301560013077,1.0464932061731815,0.1244561622377877,0.07163340225815773,0.06483194126633696,2.1039281939156353,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",140,6.563611015677452,4.663305759429932,4.454401969909668,0.913266371935606,0.03183336846687383,1.020237397402525,0.14008580944829088,0.07075374945998192,0.06387497484946088,1.9883424248546362,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",150,7.025200293865055,4.5167341232299805,4.308408737182617,0.9024377539753914,0.04079148485033005,0.9891075603663921,0.15856811572216778,0.06971876695752144,0.062282854113179666,2.055277652805671,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",160,7.500840359600261,4.510575771331787,4.241783142089844,0.8979175128042698,0.03880577424101063,0.9752441346645355,0.16091760442504005,0.06706678867340088,0.061927105540579536,2.2306392120663077,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",170,7.971918122144416,4.399102687835693,4.121989727020264,0.8953586146235466,0.04027352050164429,0.9628761745989323,0.16107060763977016,0.06458354368805885,0.05629703785990518,2.233242114074528,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",180,8.44338885275647,4.399784088134766,4.055747032165527,0.8919705301523209,0.04655915992075152,0.9358718618750572,0.1559572289610048,0.06463299691677094,0.059774556402948854,3.5061905321199447,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",190,8.894709549611434,4.2864251136779785,3.9696977138519287,0.8864729553461075,0.04353565442263489,0.8687185309827328,0.14310864212936786,0.05309665948152542,0.04863613995747818,2.038753364002332,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",200,9.349482635734603,4.28887414932251,3.954789876937866,0.8912046030163765,0.04757932602090713,0.9683191291987896,0.2180856670012135,0.07008818164467812,0.05529740864984845,2.161064524902031,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",210,9.815851871855557,4.166292190551758,3.8717846870422363,0.8867809623479843,0.051838903924775304,0.903039213269949,0.17706475006139044,0.06040685251355171,0.05926454394003135,2.39563742489554,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",220,10.27716436679475,4.195374011993408,3.781322479248047,0.8784478046000004,0.047590128943969454,0.8482864759862423,0.14216718967105246,0.04960023984313011,0.042093786023971784,2.3041695330757648,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",230,10.732906801858917,4.096522331237793,3.6657767295837402,0.8728338032960892,0.05010876956719276,0.8474209643900394,0.12955337517748228,0.05126390978693962,0.047790785821179106,2.1841811281628907,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",240,11.18515487993136,4.141009330749512,3.665358066558838,0.883757684379816,0.043898204531566384,0.877717275172472,0.14684492155617881,0.0562513992190361,0.05218346282994635,2.2058016899973154,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",250,11.650950686307624,4.083846092224121,3.6540350914001465,0.871126938611269,0.05248150174739982,0.8400285430252552,0.12653559211120752,0.0530611015856266,0.04940797584284667,2.3887801258824766,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",260,12.114934314507991,3.9785287380218506,3.4917593002319336,0.8742972910404205,0.04745474567762827,0.8525796234607697,0.1301941882418168,0.05237990990281105,0.04555479804875175,2.4195691091008484,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",270,12.577169777825475,3.9164583683013916,3.4443209171295166,0.8677079007029533,0.04801703402779062,0.8059292733669281,0.12181670657835346,0.0454227589070797,0.04496131606134032,2.4627855659928173,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",280,13.04519113455899,3.9793336391448975,3.4234156608581543,0.8726824820041656,0.04552996013592361,0.8376286886632442,0.15039003473079163,0.05180977284908295,0.05140041197341017,2.4957500679884106,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",290,13.506452789530158,3.8649773597717285,3.3513050079345703,0.864439744502306,0.05152710466385729,0.8149989582598209,0.15284031299896547,0.053034886717796326,0.05926724562429961,2.5091075231321156,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",300,13.969065234763548,3.8855695724487305,3.314129590988159,0.8652352541685104,0.04390822778860065,0.8211488462984562,0.1484942343198762,0.050669338554143906,0.04439953894758131,2.502495266031474,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",310,14.431279599666595,3.8016583919525146,3.236525535583496,0.8624599725008011,0.05094275862597765,0.8087075315415859,0.1353313112180131,0.04630665108561516,0.04039530552024856,2.4617305248975754,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",320,14.882417544722557,3.7849323749542236,3.1691582202911377,0.865173764526844,0.05011570137470219,0.8372190892696381,0.11275989283326152,0.05280124768614769,0.045711715481032755,2.3337906629312783,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",330,15.33191068097949,3.7506296634674072,3.1586899757385254,0.8665448166429996,0.04077591912400272,0.8110363781452179,0.12084840858537677,0.04356125742197037,0.04083591570857952,2.3908843270037323,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",340,15.795724728610367,3.7716846466064453,3.143554449081421,0.8667600192129612,0.038345970853011425,0.8500193618237972,0.14251599326558956,0.05563850328326225,0.05521632613448616,2.5475794340018183,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",350,16.259318242780864,3.7111897468566895,3.0589210987091064,0.8623382411897182,0.04283480181198595,0.8207896128296852,0.13572838972537,0.04722939059138298,0.04221709577867763,2.6668127209413797,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",360,16.71999426279217,3.6891024112701416,3.062805414199829,0.8656297326087952,0.04307773989872071,0.8082749843597412,0.14129639807818828,0.04701203107833862,0.039962557421402964,2.562985154101625,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",370,17.177569846622646,3.646897792816162,2.9955453872680664,0.8576682358980179,0.046631683191911744,0.8125977590680122,0.1526157998820098,0.04427217319607735,0.03587476119460054,2.5968284599948674,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",380,17.634923157747835,3.6484456062316895,3.002504587173462,0.8586713671684265,0.05532814925275865,0.8094116449356079,0.12877097969134113,0.04602324590086937,0.03393505545537458,2.647977188928053,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",390,18.096816735807806,3.5534920692443848,2.8790528774261475,0.8582490682601929,0.03974498620383974,0.7955472357571125,0.12714871301024616,0.0424978993833065,0.02996092205245776,2.6560386908240616,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",400,18.561564353993163,3.5627822875976562,2.935633659362793,0.8512259945273399,0.039824417539376815,0.816703550517559,0.12779234132570003,0.04784660413861275,0.03976912087430119,2.6210041558369994,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",410,19.02195799467154,3.475369691848755,2.8775699138641357,0.8598371222615242,0.04293164501110221,0.817610789090395,0.13907847716813684,0.04497991129755974,0.038128359884831965,2.481706224149093,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",420,19.473349075298756,3.4762654304504395,2.80649995803833,0.8547644466161728,0.03993231673003239,0.8175532519817352,0.1711693709929596,0.049209125339984894,0.04866688000271198,2.603635085048154,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",430,19.93685720884241,3.5349738597869873,2.8416831493377686,0.8648091666400433,0.04602956316419062,0.824883870780468,0.15997099447768764,0.04591892659664154,0.030129713302455164,2.717300206189975,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",440,20.400054267141968,3.615325689315796,2.8140902519226074,0.8543105572462082,0.05112849314007796,0.8230209276080132,0.1369101784919832,0.05094822123646736,0.04299084990754664,2.7039312089327723,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",0,0.001428837887942791,7.061976432800293,7.107519149780273,0.9649948477745056,0.010960409962452629,1.9640005230903625,0.2549464441528075,0.2460838109254837,0.14321909881447695,0.9833942579571158,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",10,0.46377723827026784,6.3412933349609375,6.407061576843262,0.9565748572349548,0.014499273055054617,1.4640570729970932,0.2049443841141832,0.14775830879807472,0.09315313526969074,1.9443384280893952,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",20,0.9258057076949626,6.144214153289795,6.14536190032959,0.9541316516697407,0.014707373942957083,1.4656746163964272,0.18224682040233106,0.1318661104887724,0.0921306836273114,2.1406621511559933,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",30,1.3938331636600196,5.920687675476074,5.963802814483643,0.9512924142181873,0.01591835393020595,1.3973015919327736,0.14314983722080585,0.12078546173870564,0.08817654047625227,2.1443293909542263,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",40,1.8564665680751204,5.810150623321533,5.828560829162598,0.9505909644067287,0.013706854354171684,1.3264957889914513,0.13569279006068807,0.10362955555319786,0.07773491798253465,2.082279035123065,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",50,2.308014118578285,5.675076007843018,5.700084209442139,0.9463495090603828,0.01752085554676021,1.3422916606068611,0.15157405202976523,0.11006873473525047,0.08169945546049398,1.9920891299843788,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",60,2.7585599394515157,5.571297645568848,5.621583938598633,0.9484864175319672,0.01751524671132889,1.315411925315857,0.16873770406310243,0.10810915753245354,0.07850496232857465,1.9402903029695153,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",70,3.218775100307539,5.45728874206543,5.468419551849365,0.9463217034935951,0.016294688070504118,1.2413393333554268,0.1232714920118711,0.09352074936032295,0.0672068128979516,2.1186568499542773,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",80,3.6787816483993083,5.353488922119141,5.346938610076904,0.9434224516153336,0.015428192853306577,1.2183418050408363,0.09930999124297193,0.09156173095107079,0.07164648660163675,2.113362570060417,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",90,4.139545534271747,5.262608051300049,5.212178707122803,0.9398517310619354,0.016264628989917387,1.2409226521849632,0.16661692864069932,0.10019538179039955,0.07458151613622685,2.138887036126107,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",100,4.605416156584397,5.086238861083984,5.057481288909912,0.9353660829365253,0.01806482725418648,1.1787398010492325,0.12636066918942465,0.08524240180850029,0.06578660740924817,2.17836693697609,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",110,5.063407309586182,5.028367042541504,4.915846347808838,0.9317652396857738,0.018205714394923668,1.1421531550586224,0.1393884480214853,0.08102241531014442,0.0663130923878625,2.1376291878987104,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",120,5.523071643197909,4.879014492034912,4.763766765594482,0.9266987480223179,0.021094175652895456,1.1174595765769482,0.12740246520799237,0.08168726786971092,0.06971159215487135,2.188482132041827,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",130,5.979860697640106,4.741600036621094,4.593395233154297,0.9220268093049526,0.020001328374531384,1.0779906213283539,0.1486493249022852,0.07804261893033981,0.06702650503353642,2.134456355823204,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",140,6.439196590101346,4.625983238220215,4.456476211547852,0.9155576638877392,0.024837463947080134,1.0480760782957077,0.11696324618717048,0.07774274051189423,0.06878293059086178,2.235896005993709,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",150,6.885237187845632,4.506103038787842,4.3112640380859375,0.9108001329004765,0.023153730584349753,1.0163947455585003,0.09206066167162272,0.07116115465760231,0.05969748540756283,2.125339054968208,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",160,7.345242212060839,4.455015182495117,4.231937408447266,0.9039027355611324,0.027697816540665852,0.9827675744891167,0.14475782991377822,0.06926816701889038,0.06195630144706651,2.2531997768674046,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",170,7.80574504006654,4.35237979888916,4.098567008972168,0.9014283046126366,0.022958183446319558,0.9763839244842529,0.09470735736819957,0.06797623634338379,0.054491279356905016,2.306825967039913,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",180,8.271187709411606,4.347997188568115,4.025655269622803,0.8997514769434929,0.026942983945157904,0.9516176246106625,0.13666421221922617,0.06506352126598358,0.07024935067955458,2.2280537670012563,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",190,8.724647763650864,4.308204650878906,3.9885573387145996,0.9004524685442448,0.02971098073045391,0.9713692925870419,0.1386051181355567,0.06866932287812233,0.06445718449064816,2.1599283108953387,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",200,9.176167467841879,4.202589988708496,3.8770804405212402,0.8901678584516048,0.03608568399829835,0.9195236451923847,0.11400494060036227,0.06241573020815849,0.06193253256588254,2.178492418024689,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",210,9.637268653837964,4.15893030166626,3.7877039909362793,0.884462546557188,0.036827877688309786,0.9052463620901108,0.15828301266886088,0.06558891758322716,0.07276200337147139,2.2891055028885603,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",220,10.099514696747065,4.118121147155762,3.714339017868042,0.8911428861320019,0.03179704332982676,0.9123205728828907,0.15643660375605534,0.059764374047517776,0.05541694228227143,2.2457540000323206,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",230,10.560869918903336,4.112666130065918,3.7039895057678223,0.8814074285328388,0.04102069471194696,0.9154843725264072,0.18027868134903913,0.06781458109617233,0.06627386757795112,2.2135522509925067,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",240,11.02097649523057,4.046855449676514,3.553239107131958,0.8678007051348686,0.04201397908337314,0.8582226857542992,0.1486450615295443,0.05415799841284752,0.050081001581656764,2.2210681419819593,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",250,11.476387715665624,3.9855270385742188,3.525540828704834,0.8721934072673321,0.041494219675948386,0.8634631223976612,0.17994714147340884,0.061400748789310455,0.07602405794063098,2.243913016980514,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",260,11.9353977907449,4.0055646896362305,3.506655216217041,0.8721630647778511,0.035600696644233626,0.8740927204489708,0.1376460110044126,0.05538751929998398,0.05244918307365537,2.2628998721484095,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",270,12.402134819887578,3.945678949356079,3.496084451675415,0.8721948899328709,0.038900621025586606,0.8818553611636162,0.1541833850179847,0.05643076449632645,0.04749716588402463,2.3644112539477646,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",280,12.863689405610785,3.925264596939087,3.3654863834381104,0.8624567687511444,0.044949930776039024,0.8279801234602928,0.14598280683244644,0.050027016550302505,0.04714509120696285,2.315525125944987,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",290,13.314311682246625,3.827575206756592,3.3228769302368164,0.8555633872747421,0.05098022623686155,0.8031170181930065,0.13271791774721,0.046484049409627914,0.04506494601135591,2.2144814929924905,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",300,13.765002643456683,3.795443058013916,3.269695520401001,0.8600774519145489,0.04739448211350554,0.8371396958827972,0.1819196782973256,0.05448208749294281,0.05339118617698923,2.142082623904571,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",310,14.224158297525719,3.8411240577697754,3.2249021530151367,0.8637690879404545,0.0439095079317398,0.8531909137964249,0.13632104914014767,0.05838892236351967,0.054357699756300386,2.3182715508155525,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",320,14.687962559051812,3.8245432376861572,3.182011604309082,0.8601097241044044,0.04475743310751955,0.8244201093912125,0.15278135950629518,0.05105695128440857,0.048888903834478,2.3730058900546283,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",330,15.147566433763131,3.7484960556030273,3.12865948677063,0.8590388484299183,0.05024187498198387,0.7952705398201942,0.12531154380496431,0.04434649273753166,0.03752371932807542,2.3493481872137636,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",340,15.592258729739115,3.777608871459961,3.1117093563079834,0.8524179272353649,0.05095377305681245,0.8207972571253777,0.17249161890315226,0.05156772583723068,0.04979103416728912,2.1913255299441516,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",350,16.04144816636108,3.817009210586548,3.101762294769287,0.8519294559955597,0.04477949204111907,0.8215209543704987,0.14512006947260087,0.05179835483431816,0.05087605988423047,2.1955855418927968,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",360,16.495159132871777,3.768324136734009,3.070770740509033,0.8574189692735672,0.04407485060286993,0.792459923774004,0.13804839045815478,0.04737979546189308,0.048661106747348984,2.4283367809839547,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",0,0.003484944929368794,7.084254741668701,7.100709438323975,0.9649051837623119,0.011435024333190722,1.9781995937228203,0.2416294328088668,0.24818727746605873,0.13904917837563677,1.3352524599758908,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",10,0.6614172069821507,6.109165191650391,6.22235631942749,0.9504091553390026,0.019845290621352527,1.4035892486572266,0.20388114632790041,0.1333482749760151,0.08430252726825549,2.693713332992047,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",20,1.315489838947542,6.056800365447998,6.03583288192749,0.9515071697533131,0.015750326553899206,1.4804208725690842,0.17872036695734692,0.13205708004534245,0.08919206464824324,3.0191733960527927,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",30,1.9723852798342705,5.907539367675781,5.91859769821167,0.9506559669971466,0.01593906633113973,1.3521965071558952,0.1399026392317369,0.1135878898203373,0.08384159940257917,2.9578060359926894,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",40,2.631738971802406,5.772193908691406,5.7267913818359375,0.9438188709318638,0.01601454343235644,1.316518411040306,0.14005900283950148,0.1041085235774517,0.08321572804291966,2.9999620449962094,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",50,3.293756047030911,5.579354763031006,5.592901229858398,0.9438190944492817,0.01936699024345006,1.2752000615000725,0.13955382887015957,0.10429572686553001,0.07970847290704206,3.066381972981617,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",60,3.9520618930691853,5.481730937957764,5.422560214996338,0.9395771957933903,0.018843110919001333,1.2404876537621021,0.1412931437484899,0.0975118987262249,0.07134193549419754,3.1660962849855423,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",70,4.61067756509874,5.291604042053223,5.184083938598633,0.9389918148517609,0.018268288807732853,1.1827450282871723,0.13535147644033108,0.08208677545189857,0.061214032165783486,3.201330162002705,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",80,5.269668206921779,5.131080627441406,5.028152942657471,0.9311215691268444,0.018375511436784384,1.129273597151041,0.10728895955040563,0.07713654264807701,0.06528426183823466,3.2151112819556147,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",90,5.926431001047604,4.933711051940918,4.7839860916137695,0.9281878732144833,0.018873778531932593,1.1225710734724998,0.13891217157055064,0.07860711961984634,0.06426533833637747,3.2480514890048653,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",100,6.583646750776097,4.748856544494629,4.622523784637451,0.9199836067855358,0.022668105139083017,1.0864743813872337,0.13522441944719293,0.0778345912694931,0.06569661756203314,3.2448410199722275,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",110,7.239085681736469,4.52736759185791,4.380197525024414,0.9095784649252892,0.027660478335997964,0.9784679338335991,0.1318194351688297,0.060123320668935776,0.0515535349581584,3.2333404570817947,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",120,7.895595891517587,4.412833213806152,4.223015785217285,0.9083234705030918,0.028614244384123813,1.0281863920390606,0.19899100174604306,0.07788852974772453,0.07070398670323999,3.340113783022389,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",130,8.556176716345362,4.239667892456055,4.026408672332764,0.8942602425813675,0.03629137693826225,0.9292176105082035,0.19968668426013425,0.06133952736854553,0.05883583820696371,3.5477690370753407,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",140,9.209190134308301,4.109028339385986,3.8322346210479736,0.8820760026574135,0.044832769392036016,0.8956814780831337,0.21048389347735819,0.060067735612392426,0.05947422110695595,3.475705101969652,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",150,9.866984602296725,3.912862777709961,3.6900360584259033,0.8871761932969093,0.03125833883592085,0.879150815308094,0.15786770051309684,0.05430930107831955,0.05138439341101293,3.662640637019649,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",160,10.517413253313862,3.812075138092041,3.5073912143707275,0.8759179189801216,0.04333834014522706,0.8753350675106049,0.14174174948068,0.0529031828045845,0.047325815819054255,3.652768993983045,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",170,11.17569893435575,3.7621891498565674,3.4083986282348633,0.8734189569950104,0.04465211518970893,0.8891056142747402,0.21855832787756146,0.060841504484415054,0.07175545712609704,3.744769108016044,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",180,11.833884353633039,3.635817527770996,3.3422200679779053,0.8746034353971481,0.046104626707228966,0.9151848368346691,0.247720008761207,0.06449612602591515,0.06156299303803663,3.956371647887863,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",190,12.492850713897496,3.531224489212036,3.126396417617798,0.8549492359161377,0.05260888426387092,0.8128212355077267,0.21335454658728842,0.048097874969244,0.047788342982064906,3.9025846250588074,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",200,13.146955095697194,3.501023530960083,3.0870776176452637,0.8658760972321033,0.05660516643501067,0.8636176846921444,0.2599493018772948,0.05828994885087013,0.055533429591957416,4.010716296033934,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",210,13.804338788846508,3.407423257827759,2.982449769973755,0.8653268702328205,0.04726488137215424,0.8123908750712872,0.16311901406424495,0.049960821866989136,0.04381711567194121,4.031076663988642,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",220,14.46243595390115,3.243932008743286,2.850836753845215,0.8592835702002048,0.052039656789958005,0.8138141464442015,0.15718760401962414,0.048512231558561325,0.03680497903828758,3.9529560619266704,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",230,15.117468284792267,3.3318846225738525,2.90358829498291,0.8599806949496269,0.053205409219682226,0.8228897154331207,0.15761636975150198,0.053998470306396484,0.05801289734097407,4.02436582010705,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",240,15.772737126681022,3.148172378540039,2.749077081680298,0.854461133480072,0.048228466191424936,0.817327156662941,0.1735468748654056,0.04416833072900772,0.024808319296616,4.1685545248910785,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",250,16.431003201869316,3.1134185791015625,2.7165980339050293,0.8540936335921288,0.04675646534756123,0.809049941599369,0.1530858341004692,0.0444825179874897,0.029263672245417722,4.0467273000394925,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",260,17.088547433610074,3.0973362922668457,2.6854591369628906,0.8602303341031075,0.0366789066075985,0.8014097586274147,0.1907393223301718,0.039621658623218536,0.021483344518532607,4.108820248045959,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",270,17.74661117850337,3.0943307876586914,2.6536426544189453,0.8651141189038754,0.05413200034488835,0.7905580140650272,0.20999893680557663,0.04052310436964035,0.027977972723483877,4.162222455022857,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",280,18.402151283458807,3.1880812644958496,2.701465129852295,0.8552595563232899,0.05577688978782215,0.7866673469543457,0.21028205686935164,0.04450783506035805,0.04009188549921308,4.224466621992178,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",290,19.05773068452254,3.1618428230285645,2.5860395431518555,0.8547028675675392,0.053287467409428226,0.7934906482696533,0.19275082430402773,0.0416865237057209,0.032843934982311626,4.318049296038225,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",300,19.710923829465173,3.0447840690612793,2.4832923412323,0.8593731075525284,0.057489787868850784,0.8120333962142467,0.21977144061428613,0.046571604907512665,0.030412445104437885,4.417320496053435,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",310,20.366427267552353,3.0743613243103027,2.4443111419677734,0.8564467839896679,0.059247987416319044,0.8869434855878353,0.2662169551184048,0.068783950060606,0.07730551446643458,4.313742456026375,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",320,21.01767727045808,3.010211229324341,2.3983564376831055,0.8570247180759907,0.06627756637888799,0.8374200910329819,0.26759815223690236,0.04999614506959915,0.039231241836544514,4.482546326005831,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",330,21.671095214318484,3.0172362327575684,2.36609148979187,0.858675379306078,0.05459084722701688,0.8531052693724632,0.23928423548832428,0.048513684421777725,0.02572611280938487,4.565721124061383,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",340,22.32420593535062,2.9820032119750977,2.426055669784546,0.8498891554772854,0.05262663294217787,0.8275230526924133,0.25764764899353226,0.04809900000691414,0.03608837057456626,4.516520643024705,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",350,22.980328491539694,2.94816517829895,2.278947353363037,0.8474403657019138,0.05376071676128939,0.8467168398201466,0.24142860316863485,0.05537785589694977,0.05195322651672061,4.396291502984241,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",360,23.62983682961203,2.9423537254333496,2.3304195404052734,0.8572364151477814,0.06014413599096445,0.805630948394537,0.22602133003836453,0.04418263956904411,0.026346949644130667,4.4798438580473885,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",370,24.284103455254808,3.0566086769104004,2.3070709705352783,0.8483556732535362,0.05891853174918137,0.8061133772134781,0.22516505320278513,0.05030101537704468,0.051894440060903706,4.4014544249512255,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",380,24.938826455152594,2.955599069595337,2.2574992179870605,0.8578789681196213,0.06072061456582139,0.7819701265543699,0.21317668349570656,0.03859473392367363,0.02477852169968647,4.552985650021583,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",390,25.59547514503356,2.92861270904541,2.228193998336792,0.8451821878552437,0.06617965480847912,0.7937393523752689,0.2352279549528207,0.04299329221248627,0.0301374824741728,4.666695730993524,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",400,26.254384304280393,2.8934476375579834,2.141974449157715,0.8446630761027336,0.05992314389746086,0.8482809811830521,0.22783797514050771,0.04947179555892944,0.029853384701955157,4.58102713804692,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",410,26.913416353287175,2.8846330642700195,2.1317694187164307,0.8564113117754459,0.05279952631702768,0.8327249214053154,0.2461849774301195,0.047390688210725784,0.03260024544497606,4.697576890932396,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",420,27.572231806232594,3.113224744796753,2.2007863521575928,0.8543424643576145,0.0556014765062089,0.8608616683632135,0.2674658316536549,0.05852287635207176,0.049948180610273285,4.726610479061492,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",430,28.228499779943377,2.969505548477173,2.099108934402466,0.8412755690515041,0.051916051525389195,0.7929015383124352,0.21144013229367106,0.04298293590545654,0.022868315687390672,4.635366993956268,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",440,28.88092574989423,3.0804502964019775,2.0949978828430176,0.8373412266373634,0.05270647938137068,0.7654492668807507,0.252907318979871,0.03757232427597046,0.022819670596220632,4.750373400980607,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",0,0.0021814809879288077,7.076992034912109,7.096212387084961,0.9642043299973011,0.010025755993671757,1.9971480667591095,0.24832170759331423,0.2523317914456129,0.13805025533799317,1.2018949799239635,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",10,0.6530981680843979,6.098165988922119,6.114718437194824,0.9504125081002712,0.016856376552082934,1.4455501809716225,0.1907548806274611,0.1303393729031086,0.08781485701839159,2.214931044029072,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",20,1.3058192000025883,6.051370620727539,6.060439586639404,0.9542262442409992,0.014163828887193733,1.4191844016313553,0.15932430662077976,0.11613748222589493,0.07501249330176381,3.1207107179798186,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",30,1.9619543763110414,5.867687702178955,5.864032745361328,0.9500142335891724,0.012728844148718313,1.41438477486372,0.11394888551072967,0.12276810221374035,0.08435051124300232,3.2266202230239287,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",40,2.6156231892528012,5.740033149719238,5.735761642456055,0.9484582394361496,0.016441714150921303,1.3235092386603355,0.1560910913064803,0.10250619798898697,0.06697229455792465,3.1803776659071445,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",50,3.269933221163228,5.593056678771973,5.548394203186035,0.945374570786953,0.01744131960529389,1.2602870613336563,0.14955951686459723,0.09526682272553444,0.06569537303524885,3.2297217960003763,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",60,3.9243355593644083,5.502357006072998,5.4031877517700195,0.938967440277338,0.02008058306138521,1.2396220527589321,0.14694104766222685,0.09435100853443146,0.07336257459911404,3.294524416909553,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",70,4.5852203875547275,5.329130172729492,5.180691242218018,0.9357642084360123,0.02131309368211061,1.1931444332003593,0.16006527026141948,0.08713960275053978,0.06392981289483572,3.3192942190216854,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",80,5.243726814747788,5.071205139160156,4.93838357925415,0.9305721111595631,0.01932509900570734,1.1234723664820194,0.14000706066657856,0.07522895559668541,0.05986248214933091,3.287879797979258,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",90,5.898577076848596,4.901339530944824,4.700248718261719,0.9245886728167534,0.023610015576952425,1.0927208475768566,0.12303233727958095,0.07689693197607994,0.06498328961763752,3.382845081971027,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",100,6.554057973669842,4.813234329223633,4.518067836761475,0.9169958755373955,0.028104097469961696,1.0363271087408066,0.14590592039829411,0.07115767896175385,0.06065378882049094,3.292015362996608,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",110,7.2139153006719425,4.677839279174805,4.379378318786621,0.9161381796002388,0.025120974786312245,1.0326245240867138,0.1904521533364299,0.07107828930020332,0.06380776910005866,3.3866471379296854,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",120,7.865725055686198,4.470149993896484,4.13413143157959,0.9088137373328209,0.02559997115911405,0.9831430837512016,0.15406738413571908,0.06444327160716057,0.04490133749897507,3.376259359996766,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",130,8.51647905644495,4.43596887588501,4.030423164367676,0.9026808328926563,0.028472362172072312,0.9398286566138268,0.16133311337484127,0.06246264651417732,0.05350658882193951,3.342304355930537,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",140,9.16809449531138,4.228201866149902,3.8198254108428955,0.8974914997816086,0.030154639487233067,0.93951366096735,0.17207560315775847,0.059188276529312134,0.04624584498376006,3.409279161016457,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",150,9.824110036133789,4.1756205558776855,3.7376606464385986,0.8895254619419575,0.02793661569416506,0.9088895879685879,0.21303760837036823,0.054621048271656036,0.048277461730731525,3.2949641220038757,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",160,10.483837452135049,4.025676727294922,3.5557358264923096,0.8852245174348354,0.030071365711004352,0.9035250768065453,0.24700358834166156,0.05696447193622589,0.038618987234175714,3.376021696953103,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",170,11.139143637032248,3.8702621459960938,3.460833787918091,0.8707586117088795,0.02875909632579897,0.8209546580910683,0.18158646747639098,0.04814840853214264,0.047568616424716784,3.505763237015344,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",180,11.79481393599417,3.7220938205718994,3.302349328994751,0.8737778067588806,0.031239133555227235,0.8224252536892891,0.15123969791330508,0.047734566032886505,0.03969457694342743,3.6358800219604746,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",190,12.438508849125355,3.6338531970977783,3.1457982063293457,0.8654178120195866,0.03726586491148231,0.8189474567770958,0.2017230383369276,0.04278859123587608,0.032886348825812074,3.7120743149425834,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",200,13.091971267014742,3.5747439861297607,3.050546646118164,0.8587625287473202,0.03198526028514205,0.8007701002061367,0.2086110872043049,0.0418706052005291,0.03433457053167173,3.6277605099603534,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",210,13.740563946892507,3.5365376472473145,2.9691810607910156,0.8641971834003925,0.04020353330681608,0.8333089053630829,0.22614415147383835,0.048496831208467484,0.05295181584874293,3.8268332079751417,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",220,14.39404840406496,3.3356194496154785,2.8506665229797363,0.8512300960719585,0.04318728565760567,0.7896667756140232,0.19176595246132333,0.043170955032110214,0.04074196884158295,3.934790896018967,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",230,15.048348276177421,3.4116082191467285,2.8031022548675537,0.859037134796381,0.046316487394626335,0.82996766269207,0.2631793346445217,0.04975045472383499,0.03520448644561931,3.977198324049823,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",240,15.705083199194632,3.3315727710723877,2.7964398860931396,0.8660313673317432,0.04478172643858608,0.8369603045284748,0.2573608514695421,0.04820011556148529,0.04030850480199645,4.114431394031271,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",250,16.36039400624577,3.2079508304595947,2.641801357269287,0.8627671524882317,0.04964138585233715,0.8108342438936234,0.2054390073921961,0.04586628079414368,0.04419114427189333,4.124331600964069,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",260,17.016403433401138,3.3030483722686768,2.598503589630127,0.8470121212303638,0.0373606853550887,0.7679121680557728,0.18698976519229557,0.03992543742060661,0.03506632803682876,4.185566997970454,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",270,17.66901393502485,3.254288911819458,2.5784149169921875,0.8585503734648228,0.049696694533722684,0.7685808017849922,0.24429649279137622,0.0368846133351326,0.024548933530083677,4.011100362055004,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",280,18.323773951968178,3.1665849685668945,2.549142599105835,0.8540346845984459,0.053907260890788906,0.8124733902513981,0.242468241968864,0.04494134709239006,0.034440251160436704,4.196165841072798,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",290,18.976386854890734,3.2410476207733154,2.4950172901153564,0.8321842402219772,0.05081566395770819,0.8024333268404007,0.24784802761589356,0.042542070150375366,0.03732552404693396,4.166089198086411,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",300,19.629825615906157,3.1522727012634277,2.391294002532959,0.8435971140861511,0.056108878378235326,0.7421744540333748,0.2559596316161621,0.034721437841653824,0.027312733731995664,4.293613287969492,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",310,20.275059842970222,3.2437572479248047,2.3829357624053955,0.8352935500442982,0.06590189291987163,0.7453201897442341,0.2522352124664368,0.0370694063603878,0.029349392421295122,4.205177163006738,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",320,20.94054374890402,3.1905908584594727,2.3736302852630615,0.8467393703758717,0.051522864514930815,0.7541549056768417,0.23378475865264212,0.035124294459819794,0.027205114111577847,4.13035144994501,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",330,21.595139967859723,3.0691258907318115,2.3158791065216064,0.8496114127337933,0.04035865251962601,0.7453511320054531,0.24980679335505526,0.03138285130262375,0.02586989313763578,4.247675282997079,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",340,22.24967547680717,3.048542022705078,2.234229564666748,0.8226936645805836,0.05273295634886184,0.7355816923081875,0.24058115291854976,0.03822433575987816,0.038529803816586154,4.340464014094323,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",350,22.909744929987937,3.0526254177093506,2.2721762657165527,0.8406965956091881,0.06137561821166719,0.7797289695590734,0.31797216658211735,0.038911622017621994,0.029774369164968943,4.524721447960474,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",360,23.562381308991462,3.147322177886963,2.1974639892578125,0.8341103419661522,0.053957857083057496,0.7653314508497715,0.278705941465194,0.03576214984059334,0.028524712048530187,4.350231874035671,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",370,24.2205994699616,3.150479555130005,2.1518280506134033,0.8331019319593906,0.054283882171836086,0.7661049626767635,0.2549653474364181,0.03875845670700073,0.03404006165935608,4.378550085937604,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",0,0.002123163896612823,7.081693649291992,7.114048004150391,0.9679877236485481,0.009655691893748227,2.012534111738205,0.28734480261391127,0.25649988278746605,0.14725464324800525,1.2281375100137666,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",10,0.6569261570693925,6.069536209106445,6.094265937805176,0.9508084841072559,0.016621078858001084,1.4209882840514183,0.16927974170850704,0.12595272436738014,0.08277072591812563,2.8566585660446435,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",20,1.3164775745244697,6.002965450286865,6.0171027183532715,0.951447531580925,0.014712783757349815,1.411742702126503,0.1609506476781741,0.12185035459697247,0.088257174526771,3.142090008012019,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",30,1.9739383744308725,5.769059658050537,5.782933235168457,0.9469606839120388,0.016523653500820745,1.3482878133654594,0.17651640379943037,0.11101707443594933,0.08424556206542741,3.0915599330328405,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",40,2.627563779358752,5.592417240142822,5.6461181640625,0.9435143657028675,0.019099447782932478,1.2840343490242958,0.1759602285652332,0.10328355804085732,0.07866210572641726,3.019648847053759,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",50,3.2888718211324885,5.474721908569336,5.471249103546143,0.9429951794445515,0.017329431753400102,1.2560656294226646,0.1446659011174376,0.09425977617502213,0.0759107572568177,3.0363833110313863,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",60,3.952783848159015,5.303887844085693,5.263245582580566,0.9394525624811649,0.016880464546772615,1.1965338811278343,0.12347288518653272,0.08674533292651176,0.06693270817307277,2.947213715990074,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",70,4.614889750024304,5.060834884643555,5.016933917999268,0.9313054718077183,0.019136181898672423,1.0873284824192524,0.12355489852636829,0.07001487910747528,0.061819307703033156,2.951049582916312,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",80,5.273588683921844,4.8943281173706055,4.777415752410889,0.9244418330490589,0.020388599329876977,1.0937877781689167,0.14730576554656194,0.07548457011580467,0.07040456037685969,2.911583098000847,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",90,5.9306797861354426,4.732632637023926,4.582313060760498,0.922089796513319,0.021851405169582432,1.0572624318301678,0.16239018411426062,0.07680311426520348,0.08044388361317067,3.1157731590792537,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",100,6.58685409813188,4.522759914398193,4.342202663421631,0.9164108596742153,0.023258730421885598,1.0297424271702766,0.1128573431082752,0.07276519760489464,0.06227914104728142,3.2267863049637526,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",110,7.24389576993417,4.442656517028809,4.193864345550537,0.9081750474870205,0.028541630679683197,0.9938301108777523,0.10247240332625973,0.06825006008148193,0.05555790361833761,3.2647045009071007,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",120,7.903253725962713,4.35351037979126,3.9965875148773193,0.899963840842247,0.030036697759308853,0.9367421008646488,0.17604594256830117,0.06771238520741463,0.07043152044225101,3.2797373819630593,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",130,8.563005734002218,4.1396684646606445,3.822762966156006,0.9008175022900105,0.038316866033655445,0.9691718891263008,0.1630293480776281,0.06963607668876648,0.06106014108983611,3.4042016440071166,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",140,9.224668723181821,4.056511402130127,3.6716952323913574,0.8912982270121574,0.03585484657785807,0.916396040469408,0.16007540453166072,0.06323973461985588,0.05958137138730293,3.5456527998903766,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",150,9.884378662332892,3.935378074645996,3.57130765914917,0.8906233906745911,0.03281280399593665,0.900406651198864,0.18646470658662134,0.06109914183616638,0.05299297939607558,3.496598370024003,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",160,10.545346545404755,3.9377591609954834,3.5117814540863037,0.8859251514077187,0.037228256831596356,0.8512819446623325,0.12345562323770629,0.054395806044340134,0.049875393104385476,3.6868652990087867,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",170,11.204460306325927,3.7621116638183594,3.3349814414978027,0.8835762441158295,0.03736961058388328,0.8709892183542252,0.16539560212802518,0.056076694279909134,0.04539499543660357,3.6107971030287445,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",180,11.863568117027171,3.793342351913452,3.2652273178100586,0.8882753141224384,0.04055901409956574,0.9013777263462543,0.18231266546695044,0.06082753464579582,0.04932912604127635,3.7619283830281347,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",190,12.523920132895,3.7374789714813232,3.2137131690979004,0.8857412450015545,0.04264593428535014,0.8751247301697731,0.1977528927417842,0.05336737632751465,0.04813235347798292,3.792243713978678,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",200,13.180337395053357,3.516080617904663,3.040066719055176,0.8749396912753582,0.053116405159151905,0.8377548828721046,0.16519463664694797,0.049539994448423386,0.03765443640258303,4.360319104976952,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",210,13.843222748953849,3.440582513809204,2.950125217437744,0.8680425137281418,0.04881653190138309,0.8294782638549805,0.16175286681415862,0.047772474586963654,0.03993684906300279,4.368753699003719,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",220,14.508786428836174,3.4401116371154785,2.8612241744995117,0.86828388646245,0.047879751074941926,0.8560374602675438,0.2035093175987428,0.050394851714372635,0.03890105170863945,3.9863580839009956,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",230,15.172913404880092,3.3507168292999268,2.807548999786377,0.8667003847658634,0.051449185391172346,0.8506454676389694,0.22964650159290254,0.05358746275305748,0.043279610971537735,4.035187429981306,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",240,15.83067283895798,3.288775682449341,2.7618064880371094,0.866913054138422,0.057279965503481585,0.8575610686093569,0.20197771568788447,0.05624350532889366,0.0493767110338013,3.984527728985995,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",250,16.49222539702896,3.297349214553833,2.716010808944702,0.8684709146618843,0.048691210091477505,0.8197892680764198,0.18413384859241594,0.05177517235279083,0.057261433919068454,4.212461335933767,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",260,17.149439597968012,3.1960675716400146,2.653810501098633,0.8594059720635414,0.05689518135091934,0.8110518269240856,0.18341070080387978,0.04565821588039398,0.03969445324247862,4.155079436022788,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",270,17.807435012888163,3.060886859893799,2.5315287113189697,0.8622739873826504,0.04666581976721856,0.8346672616899014,0.24791757767215075,0.049484383314847946,0.03874017654630642,4.320081121055409,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",280,18.46983127389103,3.3032078742980957,2.6263463497161865,0.8614507392048836,0.05499880508879911,0.8158705234527588,0.22312079157426062,0.04912872239947319,0.04271529588559216,4.256971405935474,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",290,19.1284072000999,3.219508171081543,2.609002113342285,0.8550993576645851,0.05411931075469088,0.8180830702185631,0.2326697066244246,0.043142687529325485,0.0380618809877102,4.437923221033998,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",300,19.789399666013196,3.2443666458129883,2.4826269149780273,0.8548269160091877,0.05871611811034512,0.8422193825244904,0.26397563538046215,0.049650534987449646,0.035972951073639875,4.351299368892796,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",0,0.002220546011812985,7.123449325561523,7.1325554847717285,0.9654870554804802,0.00958307985130697,1.990874819457531,0.2500350537296056,0.24974704906344414,0.13369861932451446,1.1933746510185301,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",10,0.6629959552083164,6.108274936676025,6.140836715698242,0.9506875015795231,0.01674813253548757,1.4063279703259468,0.1893308986461391,0.12726164422929287,0.08814218502359919,2.538860373897478,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",20,1.315644393209368,6.0279130935668945,6.058404922485352,0.950074166059494,0.015651392051133545,1.4338674172759056,0.19347597976154746,0.12098810449242592,0.07534432120209202,3.116530808969401,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",30,1.973661348107271,5.82823371887207,5.856776237487793,0.9498908706009388,0.014618541832298161,1.372205227613449,0.16254350264289646,0.11060358583927155,0.07081637359552977,3.094317426905036,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",40,2.633851402089931,5.722172737121582,5.742143630981445,0.9454629085958004,0.015793587598715974,1.3167956918478012,0.14993162022241074,0.10301044769585133,0.07574076120718459,2.904842821881175,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",50,3.2921533019980416,5.615190505981445,5.578484535217285,0.9449496045708656,0.01392998232353747,1.2981562092900276,0.15475245764905476,0.09972426667809486,0.06595908806253127,3.062471941113472,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",60,3.944773012888618,5.473884582519531,5.406272888183594,0.9403071887791157,0.01758087488665532,1.2372841387987137,0.17606320656414415,0.09431913122534752,0.0710468278976844,3.0057860219385475,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",70,4.605180431040935,5.247011661529541,5.209485054016113,0.9396982528269291,0.01797086005992698,1.2034917138516903,0.15784330659529283,0.09008480608463287,0.06176059690987793,3.0623849048279226,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",80,5.261208223295398,5.114686489105225,4.985286235809326,0.9313057623803616,0.02153683721326444,1.1613027267158031,0.18158783352547803,0.08054784685373306,0.06276228641927264,3.0997779481112957,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",90,5.920116050052457,4.957923889160156,4.807114124298096,0.9278264231979847,0.020360991858837083,1.120651327073574,0.15360971520635167,0.08404064178466797,0.07164025193465509,3.3169521340169013,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",100,6.576720629236661,4.745715618133545,4.515120029449463,0.918794795870781,0.015319829265966464,1.0510592982172966,0.1366261664072822,0.07382898405194283,0.06600042352586206,3.257987817050889,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",110,7.235737582319416,4.544483184814453,4.305813789367676,0.9136349074542522,0.01820634473759673,1.001833900809288,0.11503691682140443,0.07095381990075111,0.0617313671235498,3.467322789132595,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",120,7.896006747032516,4.387982368469238,4.107253074645996,0.9078685343265533,0.023049099564571397,0.989754144102335,0.15169121576562772,0.06638679653406143,0.056193500212109435,3.497744180029258,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",130,8.556647010031156,4.15458869934082,3.8561203479766846,0.8961168676614761,0.02211872947056094,0.8817938268184662,0.11839983707147189,0.05123266577720642,0.04555122537384627,3.5995553501415998,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",140,9.211776843643747,4.005032539367676,3.674386739730835,0.8914550542831421,0.027612699227092925,0.8901741057634354,0.1480005764652172,0.05411457642912865,0.04399601293888308,3.5335439210757613,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",150,9.867792317993008,3.8802146911621094,3.568206548690796,0.8828142732381821,0.04054476894548028,0.8734844326972961,0.1708830962698882,0.054736342281103134,0.044921149539944014,3.7963533690199256,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",160,10.524109643301927,3.8339481353759766,3.4674558639526367,0.8810727410018444,0.03399853651032848,0.8747662492096424,0.1311225036801091,0.05368353798985481,0.04498586882497453,3.7562196729704738,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",170,11.182689341134392,3.6328704357147217,3.3449037075042725,0.8766795843839645,0.04186481830961118,0.830826211720705,0.1486752443333426,0.04467833787202835,0.03434773269705813,3.823762916959822,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",180,11.84525508095976,3.502577543258667,3.2040812969207764,0.8843070529401302,0.04153523547733944,0.9107732065021992,0.199752985906899,0.06512711197137833,0.06138899380992023,3.876675878884271,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",190,12.508293977589346,3.4086244106292725,3.052827835083008,0.8707250021398067,0.04634708495892586,0.854252465069294,0.14687888000182325,0.051240224391222,0.03669327362863337,3.968272604048252,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",200,13.168500761850737,3.445223331451416,3.076838493347168,0.8751183785498142,0.04571932964325678,0.8285077810287476,0.1657395229167821,0.050021473318338394,0.04701341453934072,3.90887541603297,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",210,13.828768102801405,3.3337388038635254,3.0154995918273926,0.8598902001976967,0.05031899658364582,0.8141113594174385,0.15692376533729094,0.048975247889757156,0.05017327256788865,4.208294340875,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",220,14.49383952759672,3.1243371963500977,2.8195910453796387,0.8590671755373478,0.05242440748810037,0.7638974562287331,0.15074970109146726,0.04306565225124359,0.03659706953282354,4.130676692118868,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",230,15.151891971589066,3.1967127323150635,2.7428619861602783,0.861265130341053,0.050324734224655915,0.786931149661541,0.1662221205507702,0.04147850722074509,0.029555063577041456,4.26093250606209,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",240,15.806831924361177,3.21002459526062,2.7914226055145264,0.8651749417185783,0.046186185268504314,0.7920615747570992,0.2144924076232181,0.051102254539728165,0.06507252384019367,4.287207395071164,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",250,16.46150749071967,3.1347217559814453,2.6653120517730713,0.8626399897038937,0.056166933150906574,0.7775617055594921,0.17588489581278363,0.041927359998226166,0.03470024715127835,4.239967809058726,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",0,0.0016929160337895155,7.095486640930176,7.106584072113037,0.9657612182199955,0.009247331279285271,1.9995078667998314,0.24210423450322716,0.25271211564540863,0.13551376613938054,1.189398329006508,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",10,0.6587999321054667,6.071009159088135,6.1545562744140625,0.9541618414223194,0.014898115871512204,1.4490429535508156,0.22154566996629058,0.13503896817564964,0.08279681401138443,2.75829465710558,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",20,1.3139341622591019,5.966796875,6.049126625061035,0.9521193094551563,0.01714834095209784,1.4319072589278221,0.24619545912404073,0.1365297995507717,0.10075746171472252,2.913908953079954,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",30,1.968286091228947,5.855708122253418,5.878867149353027,0.948640264570713,0.017510151050421856,1.3946997597813606,0.18552639197575735,0.11683065816760063,0.08815062733782965,2.97332251095213,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",40,2.6300292280502617,5.750303745269775,5.729146480560303,0.9481215290725231,0.01708803620751777,1.3370912373065948,0.14464711927743293,0.10944084823131561,0.08367096904942092,3.0433478399645537,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",50,3.285856038564816,5.579220294952393,5.5207929611206055,0.9441844299435616,0.01853740622158656,1.2827331125736237,0.16115445561770808,0.09686275944113731,0.0756314609419937,3.13652271986939,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",60,3.9424190395511687,5.3433146476745605,5.318796157836914,0.9370437264442444,0.018217127776526305,1.197264939546585,0.154448724960778,0.08765393495559692,0.06643499761302633,3.059812219114974,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",70,4.598015768453479,5.2001261711120605,5.102980613708496,0.9307848550379276,0.022203294426453305,1.1656358987092972,0.16784453453747644,0.08408026769757271,0.06361183767633156,3.195134347071871,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",80,5.252669457346201,5.034822463989258,4.895807266235352,0.9280396960675716,0.0172989301994511,1.1173661686480045,0.15430151776179388,0.07830634713172913,0.0587203093904858,3.19768505403772,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",90,5.904787573264912,4.821829795837402,4.6580939292907715,0.9224847927689552,0.02237490007509614,1.0810873806476593,0.16422237102222592,0.07479410991072655,0.06305861667753729,3.354367177002132,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",100,6.55734991049394,4.684978485107422,4.469191551208496,0.9143672101199627,0.023595915448358085,1.0500906147062778,0.17889799978343485,0.07027416303753853,0.0528349211582575,3.2657999808434397,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",110,7.215066476492211,4.451676845550537,4.235282897949219,0.9072593003511429,0.02521855249065298,0.9463937655091286,0.1418670930967754,0.059984251856803894,0.05519402572282715,3.530573393916711,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",120,7.866408598842099,4.506363391876221,4.174700736999512,0.8954769894480705,0.032470943504748925,0.9067756272852421,0.1237948870086858,0.056634675711393356,0.04820757040643213,3.4088722348678857,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",130,8.526258253958076,4.2361249923706055,3.959515333175659,0.8988678492605686,0.02790569980580252,0.9336428493261337,0.1641065215357064,0.06166224181652069,0.05275678055569918,3.4434444159269333,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",140,9.182234230916947,3.9860646724700928,3.712827444076538,0.8839417994022369,0.037666147170282833,0.8536998219788074,0.15285242720439468,0.048560161143541336,0.047950911060072245,3.4192096390761435,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",150,9.834523003548384,3.997230291366577,3.653130531311035,0.883114967495203,0.03482204181102605,0.8786879740655422,0.11326074449866458,0.04908430576324463,0.038200741336482574,3.493766383966431,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",160,10.491289607482031,3.833760976791382,3.4581141471862793,0.8778672032058239,0.03553260182649633,0.8807952888309956,0.20348927143728857,0.06075584888458252,0.06875024742549332,3.5113098020665348,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",170,11.14835043461062,3.705169916152954,3.325831174850464,0.8719786517322063,0.03963006433181905,0.8433282785117626,0.19721289686023558,0.054930031299591064,0.05817464019560231,3.7266167991328984,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",180,11.80340110254474,3.6535463333129883,3.232699394226074,0.8724429756402969,0.0370183871109134,0.8421521969139576,0.13459419069460746,0.04862559959292412,0.03863390708471125,3.8063894570805132,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",190,12.458727275487036,3.5925040245056152,3.1729283332824707,0.8696919195353985,0.03304945138898327,0.8568304441869259,0.13131002840392186,0.049770765006542206,0.035830854270264924,3.7602352839894593,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",200,13.116898755542934,3.4554662704467773,3.0873899459838867,0.8712425418198109,0.03685817924973956,0.8162453323602676,0.17614521793892302,0.041687801480293274,0.029760690895098828,3.754838563967496,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",210,13.77387647680007,3.534724473953247,3.0520291328430176,0.8625839352607727,0.04144840879480228,0.811801977455616,0.1713219239114284,0.045808207243680954,0.03822050952995127,3.9162385649979115,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",220,14.434016236569732,3.411144495010376,2.8855972290039062,0.8536410257220268,0.03856916619626009,0.8178350143134594,0.1690636092344096,0.04079288989305496,0.02425308349597493,4.0415034929756075,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",230,15.092133325757459,3.315279006958008,2.7846927642822266,0.8508000560104847,0.05696306653831501,0.7706318199634552,0.14332097331753121,0.038565319031476974,0.032635601432585086,4.077851386042312,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",240,15.741417957935482,3.277832508087158,2.8159561157226562,0.8538536094129086,0.047075170688832595,0.7903308849781752,0.21377523861143635,0.0411081537604332,0.03841931326489285,4.158236638177186,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",250,16.393665799638256,3.33077335357666,2.806156635284424,0.8525140956044197,0.051779789011856425,0.8327807560563087,0.17186993656851188,0.047574233263731,0.04257768737584711,4.114351983880624,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",260,17.05139460391365,3.2206389904022217,2.6666555404663086,0.8462818264961243,0.04992309873718292,0.76750523224473,0.17352571562777397,0.038771454244852066,0.028884551808277485,4.1883615320548415,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",270,17.703848760807887,3.1781814098358154,2.6764988899230957,0.8534208200871944,0.04766984356562072,0.8196670599281788,0.18268549537428547,0.049652308225631714,0.047744742402558396,4.206462444970384,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",280,18.356546531431377,3.16981840133667,2.5699448585510254,0.8514684811234474,0.04169900174873845,0.7775463573634624,0.19388943253797883,0.04224250465631485,0.03455460640190059,4.591729272855446,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",290,19.015528326155618,3.055171489715576,2.4560787677764893,0.8483286127448082,0.049394920121394396,0.7735778484493494,0.18328514108421046,0.04084281623363495,0.03572913056400846,4.267879679100588,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",300,19.671009754762053,3.0947253704071045,2.4502735137939453,0.844668734818697,0.05655285958201294,0.7754937820136547,0.19619073549309154,0.04336059093475342,0.04249211777959187,4.368132141884416,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",310,20.329434780636802,3.06801438331604,2.431039571762085,0.8453664518892765,0.051520115389785244,0.7545826397836208,0.15329327638226983,0.03727760538458824,0.023783570477500786,4.403681517811492,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",320,20.98684376780875,3.0420949459075928,2.4167327880859375,0.8414902500808239,0.03797336633657593,0.7837374061346054,0.21647306457804164,0.04092859849333763,0.031780871008433825,4.5159010309726,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",330,21.642673091962934,3.081409454345703,2.4079079627990723,0.863312654197216,0.04220601639596065,0.752081386744976,0.18716753914675083,0.03435676917433739,0.02280768067170818,4.604323195992038,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",340,22.303298867773265,2.991093158721924,2.3956055641174316,0.8457013741135597,0.044199802991574486,0.7706591188907623,0.2286362427625071,0.04201103001832962,0.04800701898271739,4.812988663092256,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",350,22.957942539826035,3.0277018547058105,2.3006911277770996,0.8499444648623466,0.04726198990603832,0.7758246511220932,0.2042664286164006,0.04086869955062866,0.03139722864046814,4.682940154103562,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",360,23.611835159827024,3.038259983062744,2.250476837158203,0.8497037664055824,0.04575189394548871,0.7853156998753548,0.24086027356838835,0.03820318728685379,0.03030235753452808,4.733163604978472,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",370,24.26843332662247,2.9407224655151367,2.181194305419922,0.8358807191252708,0.04750337448575722,0.7607402950525284,0.2074638067820331,0.038721244782209396,0.03037849127218681,4.5679809839930385,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",380,24.926251103868708,3.117354393005371,2.2270469665527344,0.8369751498103142,0.04052914469748518,0.7491904217749834,0.20938564622624517,0.03633386641740799,0.02853630106845344,4.440230529988185,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",390,25.582650877768174,2.8922014236450195,2.1697707176208496,0.8406075946986675,0.04266259712142093,0.7786108609288931,0.1946875348914358,0.04330779239535332,0.03964766722157876,4.580221526091918,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",400,26.241906909039244,3.087627410888672,2.191866397857666,0.8469843156635761,0.052730013101542204,0.7713511325418949,0.17857976603334708,0.04469245299696922,0.040957682789258085,4.854872497031465,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",410,26.900122571270913,3.070150852203369,2.1401052474975586,0.8302935436367989,0.05076440028962268,0.7886943146586418,0.22785216172216882,0.040894560515880585,0.031091941452309164,4.718513906002045,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",420,27.551874155877158,3.0524067878723145,2.1675124168395996,0.8335283137857914,0.046100641645819895,0.7286419551819563,0.19277308051562939,0.03596688061952591,0.026318216763906895,4.875694554997608,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",0,0.0017423180397599936,7.069035530090332,7.102709770202637,0.9661588296294212,0.011166930262773357,1.9660950750112534,0.2811219422337734,0.24549267813563347,0.1456464724249449,1.2472434369847178,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",10,0.6685082919429988,5.991365432739258,6.055062770843506,0.9505926854908466,0.014506279454778889,1.4350177720189095,0.18342139124919105,0.12634248286485672,0.08395888512533704,2.2159303061198443,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",20,1.3350286069326103,5.991241455078125,6.0290727615356445,0.9488547965884209,0.017693559781633183,1.4110510051250458,0.19174463037199949,0.12617941945791245,0.08302381091160087,3.522346714977175,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",30,1.995025540702045,5.842141628265381,5.863973617553711,0.9463181234896183,0.018781452925042033,1.3609656989574432,0.15964429899490826,0.11239855363965034,0.08119098041631562,3.3448329858947545,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",40,2.6515196221880615,5.698580741882324,5.696706771850586,0.9445495381951332,0.017174270051073866,1.29999178647995,0.1411140104004227,0.10507162287831306,0.07892948722404226,3.46504671103321,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",50,3.3106495002284646,5.504426002502441,5.5249457359313965,0.9427224323153496,0.018736025413940354,1.262740209698677,0.14167796345689318,0.09772424772381783,0.07717777727178793,3.0976294439751655,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",60,3.9689613352529705,5.402762413024902,5.387654781341553,0.9401885941624641,0.016031913298261695,1.1951438747346401,0.1140332248122014,0.08506324887275696,0.07608029354070511,3.137952656019479,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",70,4.629218079149723,5.15845251083374,5.154312610626221,0.9321919232606888,0.02132993529832046,1.1677332781255245,0.13839006234661863,0.0834660492837429,0.06738609827564698,3.2057461189106107,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",80,5.28826666623354,4.972907066345215,4.903179168701172,0.9227924309670925,0.024146798911112585,1.0874779485166073,0.1175309540845513,0.07685908675193787,0.0671525851600372,3.257975755026564,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",90,5.94423489831388,4.824483871459961,4.685450077056885,0.9167465977370739,0.023200663175533886,1.0685883611440659,0.14045691434548016,0.07627904042601585,0.06898781827232292,3.1506145109888166,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",100,6.603545749792829,4.655185699462891,4.498473644256592,0.9082929827272892,0.028395782756477007,0.9833596982061863,0.12062080978389036,0.06760991364717484,0.06246556446508222,3.295951545936987,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",110,7.262930355966091,4.545266151428223,4.295050621032715,0.9041758552193642,0.030653990126177623,0.9714199267327785,0.152642292176468,0.07039924710988998,0.06628495543287163,3.205290013924241,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",120,7.920830501941964,4.4139404296875,4.124732971191406,0.8966374732553959,0.032874525148651586,0.9516505673527718,0.129593785144413,0.06816614046692848,0.05907875818788771,3.344056508038193,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",130,8.577799760038033,4.338386535644531,4.015833854675293,0.8973687253892422,0.027233357221351357,0.9504435136914253,0.13048828162802606,0.06613302230834961,0.05467453992392141,3.4467458119615912,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",140,9.240757503313944,4.28433084487915,3.950251579284668,0.8927319757640362,0.031321255403538584,0.9092055149376392,0.16668279654481855,0.06405898183584213,0.06032890670367067,3.3583275710698217,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",150,9.895688877440989,4.150759696960449,3.771914482116699,0.8882434815168381,0.039679767749194614,0.9036934040486813,0.18074269444832597,0.059599511325359344,0.04897718970407821,3.5124118109233677,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",160,10.55119363218546,4.114680290222168,3.6867527961730957,0.8746033571660519,0.036488401428078014,0.8828048892319202,0.18441975161646898,0.05502980574965477,0.046410236272678214,3.3862795741297305,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",170,11.201896209502593,3.8951165676116943,3.486053228378296,0.8765589781105518,0.03496544082894511,0.8790176212787628,0.15802984361029618,0.05541297048330307,0.048411197598609165,3.8169552460312843,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",180,11.85771581926383,3.8599705696105957,3.383000612258911,0.872041791677475,0.035874938970541226,0.8410231396555901,0.15751070223748606,0.054116662591695786,0.052160753616837995,3.6726901778019965,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",190,12.514074981445447,3.7786362171173096,3.2502870559692383,0.8652352578938007,0.04146719167225696,0.8521274216473103,0.1854273782781465,0.053637322038412094,0.04858540738132293,3.799368554027751,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",200,13.166935584507883,3.6170639991760254,3.1693649291992188,0.8621859513223171,0.04397026983867931,0.8153245225548744,0.13510951802214455,0.0477469265460968,0.04077722039283655,3.920183670008555,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",210,13.814111705170944,3.516538143157959,3.0653765201568604,0.8762226477265358,0.0359397424415736,0.7900365423411131,0.1439643565684629,0.04365464299917221,0.035109538750582606,3.8030019539874047,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",220,14.473416656022891,3.438725471496582,2.939443349838257,0.8634923845529556,0.04343722807441055,0.8392278738319874,0.14988547630990845,0.05179246515035629,0.04103637030546198,4.091423973906785,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",230,15.137517982861027,3.5322470664978027,2.9403467178344727,0.8731405436992645,0.042206829105087135,0.812199492007494,0.1498525347703393,0.05016233026981354,0.05544074042635428,3.853065558942035,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",240,15.801594391232356,3.3166849613189697,2.774390459060669,0.8636785186827183,0.03686008520331874,0.7980054393410683,0.17245621421054794,0.043862976133823395,0.03225855086494881,4.081638648873195,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",250,16.46123082842678,3.224947214126587,2.7499146461486816,0.8643778599798679,0.04056635827544674,0.7836105525493622,0.14884299209596982,0.04249987751245499,0.03327152839372374,3.9680658630095422,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",260,17.127644654596224,3.281034469604492,2.698941469192505,0.8496427163481712,0.04766650266984649,0.7664121724665165,0.1346208077063066,0.03990820050239563,0.031895766986736525,4.061259591951966,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",270,17.7949236324057,3.223133087158203,2.657637596130371,0.8684390895068645,0.05640067891546632,0.8331283405423164,0.2205369929644498,0.051545560359954834,0.041515585464529714,4.138934693997726,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",280,18.451341153588146,3.190366506576538,2.6339774131774902,0.8546758964657784,0.05898817108683472,0.7831216864287853,0.1636730797652103,0.04537850245833397,0.042582168012716456,4.168084193021059,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",290,19.111682950984687,3.2435238361358643,2.5532822608947754,0.8481761738657951,0.05325396150612912,0.782824020832777,0.2307860312494145,0.04631665349006653,0.04978918454781151,4.225217066006735,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",300,19.766528589418158,3.0903713703155518,2.489508628845215,0.8425011970102787,0.056189966661233645,0.7707377094775438,0.1718495792340039,0.04401304945349693,0.040180320579966095,4.142062819097191,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",310,20.426677868003026,3.136075019836426,2.4398252964019775,0.8544339202344418,0.0556613473138328,0.7338201738893986,0.14831758762624073,0.03932112455368042,0.04133464405456291,4.394384118029848,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",320,21.08478947216645,3.133584976196289,2.4192607402801514,0.8437192216515541,0.04401122141672596,0.739350413903594,0.17123497030600343,0.03968144208192825,0.048589459797129426,4.30689626885578,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",330,21.740038914140314,3.1445839405059814,2.3478240966796875,0.8384683020412922,0.055919924291387146,0.7329756245017052,0.20112941675418555,0.03644527494907379,0.03160457526697878,4.325550596928224,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",0,0.0017508037853986025,7.08800745010376,7.114710807800293,0.9656384512782097,0.009830548454215661,2.0164531767368317,0.26442116133159277,0.25728229619562626,0.13878451099251787,1.1719634998589754,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",10,0.6622461816295981,6.040472030639648,6.159060478210449,0.9528827704489231,0.01547804204003058,1.4219841063022614,0.1794798438428304,0.12973331473767757,0.0927049168459344,2.4024281438905746,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",20,1.3224377133883536,6.109005928039551,6.116331100463867,0.9509313255548477,0.0146031572023318,1.3906698748469353,0.16441355004116096,0.11535664275288582,0.08490305698524703,3.212475497974083,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",30,1.9823701761197299,5.872776985168457,5.889896392822266,0.9478462487459183,0.01612746022450326,1.3862362951040268,0.13315467243524098,0.11477916315197945,0.08586836661822116,3.1128476520534605,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",40,2.643399944063276,5.808105945587158,5.76707124710083,0.948670607060194,0.018982620779584042,1.332100123167038,0.14473541467160386,0.10498368740081787,0.08043624695599391,3.1719864138867706,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",50,3.298851712141186,5.598066329956055,5.582458972930908,0.9457109831273556,0.016342207031941853,1.2846873477101326,0.11700238889169783,0.10043072327971458,0.07682501048634631,2.969862376805395,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",60,3.9527094392105937,5.415610313415527,5.397940158843994,0.9435116834938526,0.01746011375662954,1.269702024757862,0.13662869620539989,0.09484479203820229,0.06841459220384405,3.149032893124968,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",70,4.60853331326507,5.319490432739258,5.252981662750244,0.9390885718166828,0.01972712109284449,1.19233737885952,0.1162109550785661,0.0865151546895504,0.06597311731337761,3.2349494080990553,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",80,5.266382951289415,5.150211811065674,5.042856216430664,0.9374394007027149,0.019057566943395356,1.1858062781393528,0.12851587163144243,0.08867865428328514,0.06981307363681119,3.241581577109173,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",90,5.923129991395399,4.866225719451904,4.791627883911133,0.9277344383299351,0.01593874159757123,1.0687366537749767,0.09650227317007253,0.06965919584035873,0.05634157455774686,3.3644666210748255,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",100,6.57809951226227,4.62048864364624,4.511661529541016,0.9189757145941257,0.022251775067900867,1.0383896045386791,0.11190456793019328,0.07283516973257065,0.059588776895821455,3.2215596900787205,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",110,7.236515172058716,4.475608825683594,4.303532600402832,0.9076572842895985,0.025161118703174667,0.9771979488432407,0.14846953214809785,0.06773282587528229,0.0596897383886516,3.254783221054822,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",120,7.882357747992501,4.3314104080200195,4.101214408874512,0.8974948599934578,0.03968915629058504,0.9615669921040535,0.1545630451741189,0.0679796002805233,0.06343851237544054,3.490523327142,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",130,8.530580572085455,4.179139137268066,3.912665367126465,0.8887633383274078,0.03571893157674747,0.8748302981257439,0.11799336308594736,0.053410403430461884,0.04945904222126082,3.52390478993766,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",140,9.17895730631426,4.070515155792236,3.801805019378662,0.8897114470601082,0.02924815969232962,0.9098033830523491,0.16108268593212016,0.06041092798113823,0.051867872134773514,3.5187489779200405,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",150,9.83217694517225,3.9159445762634277,3.626143217086792,0.8744485415518284,0.04487536258109448,0.8858207352459431,0.1476047166938263,0.05832284316420555,0.04657731706573382,3.5838484088890254,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",160,10.481939621036872,3.7810633182525635,3.4987640380859375,0.8764630407094955,0.04194254054525744,0.8404969088733196,0.22338027248494827,0.057288624346256256,0.0786817649538746,3.760202976176515,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",170,11.12829001294449,3.6009163856506348,3.302436351776123,0.8770757801830769,0.029685783804860817,0.8339110724627972,0.1530912539255534,0.051057346165180206,0.06545076693853491,3.841146209044382,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",180,11.778060731245205,3.511897563934326,3.2130136489868164,0.8642258048057556,0.0411921382270147,0.8221817873418331,0.1676033820431977,0.04714960232377052,0.036841395256678296,3.710841604974121,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",190,12.427804803010076,3.451944351196289,3.098127841949463,0.8582747206091881,0.04134329226225236,0.8087601326406002,0.1589351681054938,0.045005347579717636,0.036773023552212014,3.9226152079645544,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",200,13.077769060153514,3.348978042602539,3.0411367416381836,0.8652032800018787,0.04327752354609397,0.8119183853268623,0.14183228012651117,0.04891061410307884,0.04296035602428515,3.938111463095993,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",210,13.727280675200745,3.2791504859924316,2.9250011444091797,0.8567504063248634,0.0391099601540637,0.81740877404809,0.19064178338750817,0.04789058864116669,0.04788999121596961,3.9639855560380965,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",220,14.377836752450094,3.255385398864746,2.855203151702881,0.8519860245287418,0.047745658246302135,0.7800323255360126,0.13512402519173808,0.03957406058907509,0.02947437873475591,4.188248655060306,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",230,15.03315412462689,3.203260898590088,2.9170053005218506,0.8634375929832458,0.0489424020790216,0.8208032287657261,0.139393066757595,0.04600464552640915,0.03616581941009336,4.2013855651021,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",240,15.681590338703245,3.1639320850372314,2.7396082878112793,0.8539129458367825,0.049915514358203426,0.797736756503582,0.16583944994316174,0.048932719975709915,0.055751545006773096,4.455272792838514,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",250,16.329403427662328,3.1383161544799805,2.700573444366455,0.8431684263050556,0.05844395828761178,0.816905103623867,0.1585623117861471,0.05133485049009323,0.05419267767641241,4.4348496871534735,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",260,16.978231579065323,3.135286569595337,2.686343193054199,0.8425350338220596,0.052348073244119886,0.7945570349693298,0.16623144482335084,0.04689759016036987,0.0372728393026525,4.440672335913405,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",270,17.630912119988352,3.227741241455078,2.61514949798584,0.8522322475910187,0.04356223123161603,0.8114071860909462,0.1653134340304485,0.04383600503206253,0.029966972589680587,4.409911246038973,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",280,18.284183155512437,3.02384614944458,2.53466796875,0.8422863595187664,0.05330684025592576,0.7899382412433624,0.13686451813042316,0.04064202308654785,0.03073725332682713,4.438172084046528,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",290,18.936722222715616,3.132941722869873,2.532297372817993,0.842921320348978,0.03437274347968085,0.8046283461153507,0.14181876906321236,0.04382527247071266,0.029506471590029806,4.604658333119005,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",300,19.58132052142173,3.068901538848877,2.435572385787964,0.8506154231727123,0.04064329849415622,0.7863533198833466,0.16174740901209242,0.04025377333164215,0.030427049544833815,4.527953014941886,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",310,20.230260921409354,3.016810178756714,2.4277701377868652,0.8496716432273388,0.045542005235590606,0.8060855977237225,0.19458616564742012,0.045862484723329544,0.031719029825631045,4.414302948163822,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",320,20.886425820412114,3.0720903873443604,2.376877784729004,0.8512560427188873,0.048644039578226834,0.8003942258656025,0.18864030334951953,0.04400574415922165,0.03183417149591332,4.493044022005051,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",330,21.540855654049665,3.068592071533203,2.3936374187469482,0.8556208685040474,0.04846156265457996,0.8026226609945297,0.20659780361068716,0.04251563176512718,0.028403242735908142,4.449058613041416,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",340,22.193747154204175,3.056882381439209,2.369048833847046,0.8420458175241947,0.03923971951137126,0.7778306007385254,0.17721817699741096,0.038258932530879974,0.01837883114399636,4.686266531003639,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",0,0.0022302030120044947,7.088068008422852,7.105721950531006,0.9675004445016384,0.010343765693256837,2.0012916922569275,0.2636080225227955,0.2521754913032055,0.14660620043094352,1.1875005080364645,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",10,0.6519210641272366,6.113277435302734,6.226646423339844,0.9530047252774239,0.015155594466691775,1.4261670634150505,0.22727718009813558,0.1328212209045887,0.08690788285542596,2.6130587288644165,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",20,1.302786277141422,6.009771347045898,6.047876358032227,0.9505633153021336,0.013794888845061843,1.3887196406722069,0.18576289964368384,0.11890241503715515,0.08489411605158545,2.926388134015724,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",30,1.9568803573492914,5.849306106567383,5.885171890258789,0.9492205046117306,0.016165611024619192,1.364463672041893,0.16340447018009824,0.11650024726986885,0.08795758538518168,3.0137037800159305,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",40,2.6076365346089005,5.73458194732666,5.713188171386719,0.9451924674212933,0.019627014984526583,1.3092187941074371,0.13749314804192156,0.10053210332989693,0.08127060494857614,3.0475144530646503,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",50,3.2560824567917734,5.612990379333496,5.592634201049805,0.9430554136633873,0.01896565771392585,1.2787777557969093,0.14244097191457727,0.09580326825380325,0.07541108711196443,3.0438631537836045,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",60,3.9076128299348056,5.421177387237549,5.379541873931885,0.9415915496647358,0.018386185485064776,1.2509544640779495,0.15037983030221927,0.09505701065063477,0.07492180733126012,2.998259574873373,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",70,4.563311157049611,5.302043437957764,5.233640670776367,0.9375615753233433,0.016687513060905147,1.1985215172171593,0.12252050320446009,0.08813204243779182,0.07366967193606357,3.0418559869285673,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",80,5.212617039913312,5.1163129806518555,5.005932331085205,0.9298076070845127,0.019796528854089418,1.1206671334803104,0.1331244109984235,0.083173718303442,0.07378264856918351,3.0475106879603118,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",90,5.8635925790295005,5.003332138061523,4.803016662597656,0.925324559211731,0.01970008119712512,1.084255151450634,0.12589226196633407,0.07453368231654167,0.06549497697233989,2.9870121020358056,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",100,6.509821505751461,4.834829807281494,4.574132442474365,0.9126013033092022,0.024570909293386758,1.0338993072509766,0.14538692767828618,0.0717848539352417,0.06512925836816316,3.0372848289553076,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",110,7.158475405769423,4.690029621124268,4.404594421386719,0.9088751524686813,0.025014738309730666,0.9994262456893921,0.1284479509701106,0.07080377265810966,0.060863003443807795,3.1661328070331365,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",120,7.807023273315281,4.568387031555176,4.216986179351807,0.9025913216173649,0.03242713121246763,0.9664180837571621,0.14160809036544797,0.06377329677343369,0.053050835351026455,3.2069258869159967,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",130,8.455187879269943,4.5001726150512695,4.104996204376221,0.8968484997749329,0.03618104142971715,0.9769390933215618,0.18444478224976082,0.07646209374070168,0.07704657671921135,3.293636413058266,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",140,9.103475701063871,4.372529983520508,3.9859204292297363,0.8917216248810291,0.03454888278146144,0.8906510695815086,0.11530841917953984,0.05519150570034981,0.051974579077948224,3.2808285909704864,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",150,9.752751044929028,4.187748908996582,3.8443992137908936,0.887234028428793,0.04177235781611309,0.9105321578681469,0.1353399831235398,0.05669356510043144,0.0442696771429126,3.4666277579963207,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",160,10.40267033292912,4.095892429351807,3.6891684532165527,0.8858608826994896,0.03885882991745701,0.920964915305376,0.18418591009054774,0.06220570579171181,0.05655469594428745,3.519768015947193,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",170,11.052667614771053,3.937974452972412,3.556933879852295,0.8718581162393093,0.047683956097366594,0.8380033746361732,0.15389028215011888,0.04874935373663902,0.04314487765085202,3.5275101920124143,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",180,11.69986643618904,3.9213452339172363,3.474426031112671,0.881378810852766,0.04808231829240933,0.8659749291837215,0.21111441423289237,0.0635608658194542,0.08424749042002673,3.609571697190404,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",190,12.353755448479205,3.6275312900543213,3.291311264038086,0.8616651259362698,0.050696052004629184,0.8276432380080223,0.19411083962500156,0.05667268857359886,0.058233177443987386,3.50721337297,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",200,13.008207759121433,3.5795505046844482,3.213043212890625,0.8661859706044197,0.049621994492506295,0.7739485278725624,0.13116957559653927,0.0367874838411808,0.029876194570062454,3.590143176028505,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",210,13.662570990854874,3.5001652240753174,3.1253621578216553,0.8687764592468739,0.04785020809298164,0.8608110994100571,0.16313572211913308,0.057589489966630936,0.05741747760226719,3.7504873368889093,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",220,14.310009351698682,3.376030206680298,2.946146011352539,0.8578232191503048,0.044786022287993874,0.8014269843697548,0.20504861822528572,0.05430908128619194,0.07061478576575021,3.7563904030248523,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",230,14.958696028217673,3.358708381652832,2.930349588394165,0.8585827276110649,0.050708137584830185,0.7848654724657536,0.17912110841147585,0.04191453009843826,0.03156058407726926,4.0062636800576,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",240,15.60904261446558,3.3739326000213623,2.8803374767303467,0.859195914119482,0.05345055715929129,0.8022479899227619,0.19188715498862555,0.05192502960562706,0.06721083562970104,4.0835797160398215,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",250,16.26037021470256,3.316188335418701,2.7817773818969727,0.8571172244846821,0.05889865038743594,0.7915110252797604,0.20294567744152295,0.045678120106458664,0.04519196407094721,3.918811515206471,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",260,16.9087581387721,3.271082639694214,2.7164459228515625,0.863800909370184,0.042118817627281216,0.784036036580801,0.11288401434348735,0.04180853068828583,0.03079935109993182,4.074218104826286,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",270,17.554817164316773,3.132366180419922,2.6196467876434326,0.8475328721106052,0.05548871577645636,0.7348077818751335,0.1086398778439122,0.031319521367549896,0.01833437936220252,4.195166159886867,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",280,18.21310914470814,3.1150081157684326,2.563795566558838,0.8470435775816441,0.050333707341043595,0.7641266956925392,0.1797232659191086,0.04069576412439346,0.042170838999267504,4.081101239891723,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",290,18.863130533136427,3.090214967727661,2.4896743297576904,0.853581540286541,0.05192246819552931,0.7778012380003929,0.16872627724084965,0.03920017182826996,0.03539856804520917,4.110882387962192,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",300,19.514284966280684,2.9629478454589844,2.4623827934265137,0.8491842821240425,0.04365885630701017,0.7721228450536728,0.19184809283890208,0.04362434893846512,0.04646360189519715,4.188767278101295,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",310,20.161715300986543,3.0780434608459473,2.461859941482544,0.8504042439162731,0.04339166464557532,0.7448986694216728,0.15640736308067563,0.03872138634324074,0.03186238370519672,4.2478516541887075,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",320,20.80817210744135,3.098510265350342,2.4091718196868896,0.839265014976263,0.0535713689839449,0.7515546344220638,0.09563527415679239,0.03970586881041527,0.0353549769944785,4.3322876568418,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",330,21.460329847643152,2.961230754852295,2.325515031814575,0.8483582735061646,0.05461663241487327,0.7954719960689545,0.19790743899416136,0.044140927493572235,0.03715892758390415,4.232311802916229,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",340,22.107587994541973,3.0295441150665283,2.3291420936584473,0.847137801349163,0.045115002001389244,0.7518261019140482,0.16942635639190085,0.03550301492214203,0.024493709748998626,4.351267398800701,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",350,22.756465097423643,3.022484302520752,2.3143036365509033,0.8440540581941605,0.04186705263827087,0.759141031652689,0.16698678898277075,0.03506023436784744,0.023718263550847617,4.525269764009863,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",360,23.404502427438274,2.94329833984375,2.171389579772949,0.8471957221627235,0.06335327005156059,0.7557579427957535,0.18136481479809205,0.03712344169616699,0.026917305691135127,4.2971703230869025,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",370,24.051922782557085,3.0387284755706787,2.3017289638519287,0.8420711532235146,0.04183416582686172,0.7891972865909338,0.23209050395372405,0.04683930426836014,0.04636431346079514,4.497272050008178,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",380,24.700245088431984,2.9563465118408203,2.2107298374176025,0.8360880948603153,0.0509132002163604,0.7590914145112038,0.19195927345595426,0.0395263209939003,0.03172702025592864,4.464749726932496,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",390,25.35244231391698,2.9834232330322266,2.1048855781555176,0.8339234590530396,0.04681718310598066,0.7298315856605768,0.17930763625465587,0.031668487936258316,0.017879006922077324,4.415160045027733,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",0,0.0020805259700864553,7.093749046325684,7.109354496002197,0.9658516421914101,0.011308381294562094,1.9709505066275597,0.254927131285608,0.24457090348005295,0.13736412319377939,1.1677320979069918,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",10,0.6593623254448175,6.055315971374512,6.115820407867432,0.95053506270051,0.019694259424360563,1.422714926302433,0.2246489903444788,0.13240232691168785,0.08583048037743934,2.3489206850063056,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",20,1.317098401952535,5.957310199737549,6.022191524505615,0.9524215795099735,0.013944075647788167,1.4284151792526245,0.15788590548937906,0.12531298026442528,0.08732702974407966,3.219721592962742,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",30,1.9713669794145972,5.841618537902832,5.894510746002197,0.9487628154456615,0.01461304144773759,1.4130489155650139,0.16095757219526852,0.12431696243584156,0.09339969921700064,3.1430022718850523,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",40,2.6282892203889787,5.698085784912109,5.700789928436279,0.9458314403891563,0.016356606404871084,1.3159856274724007,0.1152491874532644,0.1046336367726326,0.07981514991752092,2.9470839730929583,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",50,3.2831752416677773,5.560415744781494,5.541829586029053,0.943664725869894,0.014193958392003157,1.2951613292098045,0.10680418158894815,0.1006982009857893,0.08184899849818236,2.828140954952687,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",60,3.9405941574368626,5.359059810638428,5.296758651733398,0.9365852922201157,0.018108762499795462,1.2021186128258705,0.14459926975484572,0.09235304594039917,0.07327084324228678,2.882926748134196,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",70,4.596655319444835,5.152949333190918,5.108067512512207,0.9327712617814541,0.015805529584826002,1.139939770102501,0.11297452283769939,0.08426124602556229,0.06758487644827225,2.922606908949092,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",80,5.248464614385739,4.9383978843688965,4.820852279663086,0.9243165999650955,0.018670173828052913,1.079167451709509,0.11350130429147946,0.07739683240652084,0.06589415836525185,3.043017493095249,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",90,5.903606104431674,4.843133926391602,4.684325695037842,0.9194035157561302,0.02338529063817059,1.0592824593186378,0.13863319754539405,0.07594243437051773,0.06492394476675105,3.0870124918874353,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",100,6.556222023442388,4.532485008239746,4.372906684875488,0.9070139080286026,0.028535264397309367,0.9584583900868893,0.13282070071100668,0.061626020818948746,0.05686218509330553,3.120287796948105,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",110,7.206621053395793,4.4487810134887695,4.22212028503418,0.8984697237610817,0.02941347519268687,0.9744478352367878,0.15481752873045992,0.06769467145204544,0.05621438147099431,3.2285007969476283,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",120,7.860710467211902,4.369312763214111,4.1812591552734375,0.8985914513468742,0.0327303203578082,0.9309013672173023,0.13434305066326105,0.060740623623132706,0.05207663996496834,3.25265323696658,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",130,8.514224553480744,4.173671722412109,3.908047676086426,0.8951419293880463,0.032901118526114166,0.9055646806955338,0.12692173753593394,0.05918526276946068,0.051422753195894566,3.2974203049670905,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",140,9.170963515061885,4.01861047744751,3.783700466156006,0.8934654742479324,0.03584044428099201,0.8769712038338184,0.1388910022404821,0.05652935430407524,0.05440230212215969,3.4398588030599058,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",150,9.819734040880576,3.924614429473877,3.630117893218994,0.88366674259305,0.036755612569536154,0.8471187651157379,0.17682725696812535,0.050766561180353165,0.04702518473002144,3.39446054212749,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",160,10.469861616613343,3.8355350494384766,3.5397934913635254,0.8851939551532269,0.039265134521155584,0.8623057380318642,0.15666501474002453,0.05311901122331619,0.04605032884685875,3.6435940698720515,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",170,11.121536906575784,3.6347908973693848,3.343949317932129,0.8823246732354164,0.044512954035461356,0.8816343881189823,0.1948922968604655,0.05875862017273903,0.0619271250990111,3.6088479380123317,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",180,11.775460414821282,3.5797276496887207,3.2692532539367676,0.8708167634904385,0.04130405223465093,0.8150446619838476,0.14814147629711336,0.04252761974930763,0.03648373466871348,3.8833598010241985,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",190,12.427772127557546,3.4318814277648926,3.1322946548461914,0.8644149899482727,0.05173399886513317,0.8122813478112221,0.19025959936728087,0.04494401812553406,0.042867328605740586,3.8123754470143467,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",200,13.0813953273464,3.469053268432617,3.0935142040252686,0.8660937622189522,0.04525816642436105,0.8157915063202381,0.13731588018833274,0.05113238841295242,0.0537980085325309,3.987912411801517,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",210,13.737640895182267,3.3264565467834473,3.0083703994750977,0.8662422448396683,0.04081254502116968,0.7691614516079426,0.167374180510312,0.037966594099998474,0.029923816708825778,4.186903360066935,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",220,14.38967917743139,3.208101749420166,2.8836827278137207,0.8554359935224056,0.05555750392081421,0.7986030429601669,0.1735170922373426,0.04557579383254051,0.035832485134679376,4.204452174948528,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",230,15.042045182548463,3.276418447494507,2.9151649475097656,0.8576055504381657,0.05093200370284617,0.8118427842855453,0.16282802998032597,0.04554899409413338,0.03857320963603922,4.280714372871444,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",240,15.700071882456541,3.1445693969726562,2.7911536693573,0.8644427992403507,0.047284978973466604,0.7872975282371044,0.169423060008912,0.04147130995988846,0.041817593001492445,4.23860100004822,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",250,16.351384855108336,3.2138350009918213,2.7645761966705322,0.862914752215147,0.05489130816385922,0.7819895409047604,0.16361973857297155,0.041939787566661835,0.04091467006704794,4.365449264878407,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",260,16.999920853879303,3.0822806358337402,2.742771863937378,0.8524453528225422,0.06265544963657299,0.7175313085317612,0.15417221888690255,0.032932743430137634,0.03407820032243653,4.4556877359282225,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",270,17.65098371077329,3.0334255695343018,2.6374123096466064,0.8552257940173149,0.05379613630140103,0.8096754662692547,0.1852865625391507,0.05136319622397423,0.05394195950572122,4.634290307993069,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",280,18.30205375305377,3.0887787342071533,2.5910696983337402,0.8558984473347664,0.05828358235129931,0.7548002637922764,0.17400974571875324,0.039945997297763824,0.030774289382931332,4.52934901509434,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",290,18.951665479224175,2.9412214756011963,2.4987056255340576,0.8570879325270653,0.05592264014750235,0.7712214700877666,0.21098793447676584,0.038066789507865906,0.028885083818237917,4.630678402027115,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",300,19.603279896080494,2.872849941253662,2.4095001220703125,0.8511997573077679,0.052400460602586955,0.7398346420377493,0.17197798402612793,0.037174444645643234,0.03143445119127086,4.534614336909726,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",310,20.253460203995928,2.9807164669036865,2.454318046569824,0.8591002784669399,0.06224099277150151,0.8177185859531164,0.24637156528221746,0.04860156029462814,0.04265312985757645,4.700447524897754,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",320,20.9067524576094,2.956123113632202,2.3634543418884277,0.8548583723604679,0.0663588464287501,0.8059876151382923,0.19923805359545854,0.04565628618001938,0.03403489171238533,4.767370210960507,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",330,21.56025183829479,2.8520796298980713,2.3218677043914795,0.8440252803266048,0.05223087122453134,0.7480988167226315,0.18387186067579595,0.03790798783302307,0.037220431590488906,4.802303266944364,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",340,22.21254414319992,2.9312167167663574,2.3342583179473877,0.8667284101247787,0.06795624864797158,0.8319990523159504,0.27342546534988327,0.049564607441425323,0.027054014270505772,4.621812411118299,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",350,22.86190880695358,2.8388218879699707,2.2560830116271973,0.8587648347020149,0.059538732442820944,0.7358535416424274,0.17405902585218905,0.0377359613776207,0.032671373812256946,4.736987002193928,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",360,23.512207582127303,2.9311671257019043,2.292276382446289,0.8667577095329762,0.048303926169619364,0.762087544426322,0.18502451707401799,0.037181269377470016,0.027307772549732996,4.807438415009528,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",370,24.16115875542164,2.761674404144287,2.185068368911743,0.8471668623387814,0.0619479302489996,0.7256872542202473,0.1921771846239873,0.03181919455528259,0.023121277885078388,4.522042204858735,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",380,24.81153962831013,2.9053540229797363,2.200648546218872,0.8410313688218594,0.05927123772735683,0.7791893817484379,0.21289299828195477,0.04469646140933037,0.04081763596488937,4.669822847936302,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",390,25.462864773347974,2.832839250564575,2.1822776794433594,0.8481748327612877,0.06499209994123825,0.7634665481746197,0.2064176235546158,0.03941529244184494,0.02812637168085068,4.7505963309668005,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",400,26.11824460630305,2.7588577270507812,2.183150053024292,0.8548856601119041,0.059863653587485424,0.7693343497812748,0.25771032794332954,0.03904375806450844,0.03067384830288747,4.695931720081717,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",410,26.770896738395095,2.8693747520446777,2.087308645248413,0.8466209918260574,0.051848470817359264,0.697913033887744,0.1789039235830632,0.03181731700897217,0.029349526716193924,4.9134048810228705,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",420,27.423121671192348,2.8177356719970703,2.0939619541168213,0.8586103022098541,0.06470056799967387,0.7622645106166601,0.22223206032580087,0.04198022559285164,0.03434021380450489,4.90524714300409,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",430,28.062895083334297,2.8814942836761475,2.098633050918579,0.8566906228661537,0.06439173202430837,0.7559833601117134,0.18767912012146026,0.03975971043109894,0.025534645219252305,4.7986687200609595,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",0,0.0017271300312131643,7.096708297729492,7.127103328704834,0.9672559425234795,0.009818687443198597,2.0100352093577385,0.2655627001319163,0.25707130692899227,0.14005563407515373,1.3927005978766829,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",10,0.6732807792723179,6.114445209503174,6.1262311935424805,0.9508367329835892,0.014603355375100591,1.4049860388040543,0.18538252697611524,0.1232970654964447,0.08096460897496022,2.652562238043174,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",20,1.325076738372445,6.03670597076416,6.061368942260742,0.9540426507592201,0.014007677777649585,1.4749795496463776,0.21617273021024805,0.13486266508698463,0.08743453731291759,2.818428752012551,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",30,1.9763895431533456,5.771951675415039,5.8115010261535645,0.9474514648318291,0.018242891666272843,1.3809967711567879,0.20114355886085405,0.11330603063106537,0.0799350897443567,3.0076949559152126,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",40,2.6284838491119444,5.666228771209717,5.700253009796143,0.9492184929549694,0.016492074388538475,1.3297164291143417,0.1646670030467698,0.11011768691241741,0.08583816292173435,3.0839518138673156,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",50,3.304335151333362,5.509415626525879,5.49730110168457,0.9393036253750324,0.016174025134993684,1.2596693113446236,0.17314506326540283,0.09504685178399086,0.07397570470777283,3.2954320199787617,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",60,3.955353263532743,5.32973575592041,5.320357799530029,0.940188892185688,0.020147126559330784,1.2433873638510704,0.1745613793284897,0.09766969084739685,0.07897686889161823,2.9909417000599205,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",70,4.607541405363008,5.125293731689453,5.116298198699951,0.9331679753959179,0.019427404033820025,1.1585896275937557,0.1477762470857582,0.08303939551115036,0.06601196298952774,2.9330998880323023,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",80,5.27653672429733,5.032985687255859,4.9232001304626465,0.9255072548985481,0.02186711717117301,1.116447526961565,0.14762078644960078,0.0807330533862114,0.06535701782438681,3.2066989799495786,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",90,5.953972888179123,4.770045757293701,4.670860767364502,0.9211419150233269,0.02059586130852685,1.0666281282901764,0.12334837835993112,0.07374690100550652,0.06205580247290126,3.444681965978816,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",100,6.603069177828729,4.630659103393555,4.449192523956299,0.9138181321322918,0.024195035769551548,0.997030358761549,0.1332720657875443,0.06860221177339554,0.06675903271743407,3.1608254378661513,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",110,7.252317038364708,4.501621246337891,4.204189300537109,0.9069529436528683,0.02310402719165452,0.9661784209311008,0.14459832998571373,0.06468111276626587,0.057907152983481716,3.2985788211226463,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",120,7.9157634973526,4.271599769592285,4.032334327697754,0.9010014757514,0.025520247577978095,0.903542585670948,0.10557678048715007,0.05773776024580002,0.052689813631192614,3.3581628289539367,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",130,8.56929474347271,4.146735668182373,3.837742328643799,0.8941636346280575,0.031278050981273134,0.8665165156126022,0.15632347632635518,0.05248007923364639,0.047169311432921454,3.362024830887094,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",140,9.224778542295098,4.077452659606934,3.710980176925659,0.8864149637520313,0.030396505012672444,0.8982465490698814,0.16630822447610602,0.06453534960746765,0.06497617544090042,3.521513525163755,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",150,9.875974114751443,3.938743829727173,3.5346646308898926,0.8864722810685635,0.024700043330836065,0.8664450868964195,0.1659826322520765,0.05406561121344566,0.05546229092698579,3.5811625758651644,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",160,10.53709131386131,3.783545970916748,3.387895345687866,0.8784199245274067,0.02903365619349992,0.8426309637725353,0.14985533732080467,0.05434171482920647,0.052197759979024844,3.8059796390589327,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",170,11.205732458038256,3.7305636405944824,3.2833738327026367,0.8781701326370239,0.03239115918385522,0.8540657721459866,0.1575137621522235,0.05524848774075508,0.049956713687736146,4.115016964031383,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",180,11.876855209004134,3.5849437713623047,3.1262431144714355,0.8643471598625183,0.029804215854434747,0.8350649103522301,0.14385784839592558,0.05072963610291481,0.04301936502160884,4.186890206998214,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",190,12.529626554111019,3.5049262046813965,3.0066275596618652,0.8661485426127911,0.030274559155979618,0.8151543363928795,0.13359196130080697,0.0466708280146122,0.04001855602159978,3.9824565961025655,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",200,13.180420564254746,3.500706911087036,2.9851369857788086,0.8691695220768452,0.034580243653431854,0.8376359716057777,0.15678915389342873,0.051515884697437286,0.045014234756548276,4.16353109292686,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",210,13.852408324368298,3.3528287410736084,2.82857608795166,0.8574838973581791,0.03473018283541171,0.7575380988419056,0.09911724384880227,0.03781858831644058,0.03124173902024928,4.171101070009172,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",220,14.520428494317457,3.294536590576172,2.8017139434814453,0.856905672699213,0.03510831075959975,0.7688101679086685,0.14735291309049892,0.03633319213986397,0.023556623769979225,4.665663491934538,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",230,15.172807645518333,3.3018884658813477,2.708282470703125,0.8576692119240761,0.03726669164116872,0.7876896187663078,0.15090996540274665,0.041495658457279205,0.033203579240015026,4.142549534095451,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",240,15.834315459476784,3.2162985801696777,2.6855478286743164,0.8503722697496414,0.04647362337159508,0.75136848539114,0.14345450611461505,0.036821749061346054,0.026023403280565554,4.475063455989584,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",250,16.486605518264696,3.3136703968048096,2.6538450717926025,0.8483927212655544,0.041303467076911515,0.7998200729489326,0.1662544462316551,0.049154430627822876,0.0521834701267636,4.337095693917945,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",260,17.13511038478464,3.123669147491455,2.566993236541748,0.8443272523581982,0.04442652757809573,0.8116984777152538,0.19343580537799362,0.047134000808000565,0.04359366672250434,4.102696544025093,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",270,17.821548715466633,3.1090879440307617,2.480583906173706,0.84878920763731,0.05476248000954423,0.7902417723089457,0.20191108892683357,0.04421918839216232,0.038954934892813854,4.815594821004197,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",280,18.479580850573257,3.0117154121398926,2.414731979370117,0.8408184088766575,0.04972235863481255,0.7750543355941772,0.18147165849919286,0.04011497646570206,0.027427191149404808,4.444298861781135,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",290,19.134017146890983,3.0971906185150146,2.38027286529541,0.8500987626612186,0.04954317501515604,0.8143077977001667,0.21280559544922423,0.05392543226480484,0.0672994318541083,4.509175521088764,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",300,19.80906189768575,3.081965684890747,2.3674516677856445,0.8426814377307892,0.0423221690045405,0.7809069156646729,0.1922155574936096,0.04269636794924736,0.03214990175889997,4.9383374960161746,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",310,20.4705875844229,3.0048470497131348,2.3240251541137695,0.840761374682188,0.05314583949424272,0.7780699953436852,0.1724959418220424,0.04672449454665184,0.04735128870523373,4.570399930002168,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",320,21.155921526718885,2.9083614349365234,2.249959707260132,0.8447307571768761,0.05309002768662108,0.7233889419585466,0.21918449664680256,0.03180299699306488,0.02139159797663069,5.014430662849918,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",330,21.810426705516875,2.868760585784912,2.2655797004699707,0.8275416642427444,0.06228207107181305,0.7344398945569992,0.17426701338875597,0.03530315309762955,0.026315334361779918,4.571522429119796,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",340,22.46313343080692,2.9682745933532715,2.2051162719726562,0.8354515805840492,0.059190644753013874,0.7779563833028078,0.2291050902281322,0.04329140856862068,0.028812951834128277,4.577360537135974,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",350,23.10887580178678,2.9898464679718018,2.205104351043701,0.8437761664390564,0.0640695299093839,0.7558636199682951,0.2090663406761533,0.037328414618968964,0.023181162018200262,4.352139429887757,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",360,23.78359242156148,2.9936928749084473,2.133586883544922,0.8311170116066933,0.0646914100608142,0.7161170225590467,0.18543840689085375,0.03604571893811226,0.031189459815268137,4.753449388081208,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.004621108993887901,7.191925525665283,7.2088518142700195,0.9688443019986153,0.01063999566890836,1.9868300929665565,0.24876994037778907,0.24986346252262592,0.1372509004208722,0.04205759207252413,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.5183411631733179,5.739398002624512,5.72659158706665,0.9503471367061138,0.01720070558882385,1.3664101958274841,0.19749193381465466,0.11155354604125023,0.08128889803207659,0.04333222296554595,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,1.031153261079453,5.261000156402588,5.1984381675720215,0.9472951591014862,0.01814211981365877,1.1977506130933762,0.17296839891518634,0.08406233415007591,0.06227694283650365,0.04357428301591426,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,1.5335514680482447,4.9706268310546875,4.880748271942139,0.9456781148910522,0.01665836294765509,1.0953774154186249,0.16376583041069098,0.06719278544187546,0.05865675841814588,0.04365889297332615,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,2.035635854350403,4.836757659912109,4.707878589630127,0.945375844836235,0.01955310420844895,1.0597714707255363,0.1817460782148978,0.05778040736913681,0.05104099297172169,0.04425121704116464,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,2.538561457535252,4.7632598876953125,4.5897955894470215,0.9402775205671787,0.018829828264529086,1.0256566442549229,0.17844874236346328,0.05204052850604057,0.04477867814462115,0.04282265203073621,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,3.0380307025043294,4.68352746963501,4.500232696533203,0.9395454488694668,0.021785306321031747,1.0146541260182858,0.17330351146445108,0.05121704190969467,0.04295872609514053,0.04257163999136537,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",70,3.540971611510031,4.6651506423950195,4.421778202056885,0.9388445243239403,0.01947064008427228,0.99129419028759,0.17723340099613527,0.04680749773979187,0.04296987051956256,0.04315637890249491,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",80,4.043166926596314,4.649269104003906,4.343380928039551,0.9356141537427902,0.02479710407261359,0.9842747636139393,0.1893035555956383,0.045262597501277924,0.04558489875310787,0.04368191200774163,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",90,4.541499224491417,4.59751558303833,4.2883782386779785,0.9377131164073944,0.02406843835109312,0.9691231213510036,0.2069881446423875,0.0384393148124218,0.03336079998613227,0.043004004983231425,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",100,5.041210219496861,4.607382774353027,4.2406229972839355,0.934784796088934,0.02690055303503601,0.9874136708676815,0.19078538257866376,0.04689258709549904,0.04793626534333235,0.044127197936177254,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",110,5.54036693868693,4.53441858291626,4.185422420501709,0.9325869083404541,0.024199737951818335,0.9622849710285664,0.20965305159651001,0.03985081613063812,0.03336051459129366,0.04376343998592347,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",120,6.040678929770365,4.528465747833252,4.146904945373535,0.9348462112247944,0.025412246634886938,0.9744821935892105,0.20202248141559864,0.04240958392620087,0.03568025379487228,0.04288499290123582,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",130,6.542211448657326,4.563240051269531,4.117323398590088,0.9345414862036705,0.027440557969035297,0.9751208610832691,0.20401497510056715,0.043219536542892456,0.03916055372981354,0.04274071508552879,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",140,7.044225291814655,4.586273193359375,4.090742111206055,0.9317950531840324,0.026180902734084188,0.9455695860087872,0.22030880554504614,0.03778481483459473,0.03404064261329671,0.042492360109463334,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",150,7.547981057665311,4.50033712387085,4.038696765899658,0.9313653074204922,0.026679466056991133,0.970823623239994,0.2229939132387107,0.04165593907237053,0.03579258566098926,0.04323707695584744,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",160,8.051099444855936,4.503329753875732,4.02551794052124,0.9333212375640869,0.0231318980793134,0.952205166220665,0.2320582258770367,0.03752848878502846,0.03214668383615863,0.04301659797783941,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",170,8.553591782809235,4.4953742027282715,3.984684705734253,0.9313640482723713,0.027856618562009834,0.9639104008674622,0.24987588978070643,0.03884965181350708,0.02986968735748735,0.04376735899131745,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",180,9.05505395680666,4.512646675109863,3.9633913040161133,0.9339314326643944,0.02572260038541444,0.9607177563011646,0.24886932435705417,0.03794196993112564,0.03375082705297263,0.043266940978355706,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",190,9.554843127727509,4.528823375701904,3.936469793319702,0.9301474429666996,0.025491127575850873,0.9631884731352329,0.2557190834243701,0.03832560032606125,0.02999932332928121,0.04413626401219517,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",200,10.054039724520408,4.4963459968566895,3.921400547027588,0.9327409192919731,0.02744950031253806,0.9696940258145332,0.25384217002987763,0.040259622037410736,0.029210339497197313,0.04427316493820399,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.003045019111596048,7.1494293212890625,7.183771133422852,0.9650285504758358,0.01491009053590711,1.9382202103734016,0.2878019502270775,0.2522720880806446,0.15269271750895866,0.04284452099818736,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.5079184820642695,5.801930904388428,5.739964008331299,0.9520259834825993,0.014841988344645023,1.3097363412380219,0.157083485377543,0.10274499282240868,0.07964016157026192,0.04257851897273213,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,1.0070855901576579,5.411808967590332,5.313116550445557,0.9473890848457813,0.015846561360333952,1.1977584026753902,0.16554849555093215,0.08029705658555031,0.061701413182829165,0.04444866697303951,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,1.5068830151576549,5.1975812911987305,5.060058116912842,0.9460138566792011,0.016384137340922998,1.1368451863527298,0.15128415866413933,0.06875068321824074,0.05327067483668795,0.043633559020236135,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,2.0113971792161465,5.080685138702393,4.893353462219238,0.9447044432163239,0.01787636004446273,1.109383199363947,0.17096261447738617,0.0634068064391613,0.046531825668085246,0.0446199580328539,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,2.5160616531502455,4.9429121017456055,4.758028030395508,0.9463191702961922,0.014948886647163234,1.0887778028845787,0.16498941818060342,0.057711925357580185,0.04410068244458222,0.04357481293845922,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,3.017103747231886,4.902891159057617,4.651694297790527,0.9440622627735138,0.018111148972595744,1.0743290148675442,0.17301444356702936,0.055717237293720245,0.04468576914251374,0.0438773229252547,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",70,3.5209917523898184,4.845001697540283,4.562145233154297,0.9433617889881134,0.017500898898027743,1.0752271376550198,0.1798069819050649,0.058887604624032974,0.045434440491077886,0.04373787506483495,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",80,4.023119175224565,4.813061237335205,4.484715461730957,0.9420179314911366,0.01805651442669831,1.062032975256443,0.20334351196771783,0.05452720448374748,0.04286990949601957,0.04372981493361294,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",90,4.523089970345609,4.800100803375244,4.4313225746154785,0.939853597432375,0.021961549490161276,1.0574190393090248,0.19365151237178066,0.053448840975761414,0.04082138934123093,0.04454987100325525,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",100,5.025019209948368,4.769548416137695,4.371938705444336,0.9396992214024067,0.02341465982080882,1.0369975790381432,0.20353158685652634,0.05334276705980301,0.04333906275566152,0.04348755499813706,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",110,5.525085478671826,4.799875736236572,4.327059745788574,0.9388755969703197,0.021971798932460145,1.009636029601097,0.19545717663312145,0.04607214778661728,0.037389189209706596,0.04350178700406104,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",120,6.026545660570264,4.752848148345947,4.27488374710083,0.9374690689146519,0.023132088859660515,1.0272765457630157,0.228849551034789,0.048963241279125214,0.038357361270798786,0.043699781061150134,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",130,6.526105293422006,4.7271294593811035,4.223997116088867,0.9362826533615589,0.020076920967718168,0.9997272118926048,0.20462198580685512,0.04617464169859886,0.03418283688355671,0.04409105808008462,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",140,7.025548488483764,4.714612007141113,4.183132171630859,0.9379885531961918,0.024412798709458634,0.9936795122921467,0.20814180625500744,0.0427708737552166,0.029503237440067265,0.04386485693976283,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",150,7.522843817365356,4.708220958709717,4.142624855041504,0.9399408847093582,0.020540388669340822,0.9991844855248928,0.208034093215499,0.04623762518167496,0.03382875006049918,0.04466519900597632,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",160,8.0235197795555,4.715616226196289,4.110132217407227,0.9328332766890526,0.026094336831162687,0.9766317084431648,0.2134692296055144,0.04294746369123459,0.03365724925424877,0.04385384195484221,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",170,8.52208428864833,4.6585845947265625,4.080577850341797,0.9346307963132858,0.029354239611468495,0.9835407249629498,0.2116428188953149,0.04378579929471016,0.03417985067908544,0.04416412499267608,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",180,9.023749393760227,4.664763450622559,4.043349742889404,0.9336274527013302,0.027067915418651646,0.9864448234438896,0.2244003669589351,0.045820754021406174,0.034462074617591855,0.04372808791231364,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",190,9.526930845575407,4.704725742340088,4.015240669250488,0.9329541102051735,0.028690309472078384,0.9754954874515533,0.21769209538801243,0.0443367063999176,0.03362449287899263,0.045234721968881786,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",200,10.025375719880685,4.747112274169922,3.9931631088256836,0.9308807216584682,0.022255775274718774,0.9734017252922058,0.21053099954679008,0.04538743197917938,0.03501003296711701,0.04443540400825441,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.002675766940228641,7.125509262084961,7.148144245147705,0.967501413077116,0.008810179513830422,1.9860065430402756,0.2781065869439377,0.24917523190379143,0.14205523447797327,0.043284868937917054,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.5054577202536166,5.8117804527282715,5.768726348876953,0.9510824270546436,0.016199257275969322,1.3326024487614632,0.1851862570697502,0.10512227937579155,0.07744715257433629,0.0438624860253185,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,1.0064476041588932,5.548591136932373,5.414032936096191,0.9481526128947735,0.018032021197191838,1.236857071518898,0.18890067890643877,0.08630669862031937,0.06797178835498893,0.04353144590277225,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,1.5060055474750698,5.28853702545166,5.111269474029541,0.9473575502634048,0.01707265456501424,1.1602779142558575,0.18180753820560222,0.07116363197565079,0.055168784850453303,0.04354024201165885,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,2.0046951615950093,5.106038570404053,4.899291038513184,0.941527221351862,0.018122354840208868,1.0888687632977962,0.19526503000749224,0.060346320271492004,0.0433559840073696,0.043637813068926334,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,2.507670984370634,4.9741530418396,4.74023962020874,0.9402176663279533,0.017815649806644462,1.048588041216135,0.19558995299185136,0.05324462801218033,0.03965354161752491,0.04401152394711971,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,3.008454601163976,4.857395172119141,4.619606018066406,0.940705768764019,0.018798246066226203,1.0225180611014366,0.21130630257056437,0.04813401401042938,0.034890078045941963,0.04442856309469789,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",70,3.508840361260809,4.828389644622803,4.522641181945801,0.9372850954532623,0.01802046207518526,1.0090319328010082,0.1979296974740266,0.04782239720225334,0.03443606671463518,0.043925448902882636,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",80,4.00984647939913,4.796304225921631,4.439706802368164,0.9360689371824265,0.019827296444561004,0.9890864305198193,0.20285690327737776,0.04568642005324364,0.03296512941923084,0.04494807100854814,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",90,4.50904946832452,4.774497032165527,4.365555286407471,0.9372570663690567,0.020459005186433336,0.9703628085553646,0.21057741049960635,0.0409199595451355,0.02822779177497747,0.04442770394962281,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",100,5.009011818328872,4.791764736175537,4.312417030334473,0.9313377290964127,0.021042168266010883,0.9756634272634983,0.2191176962571618,0.04279836267232895,0.030023469062085213,0.043814396020025015,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",110,5.508820569491945,4.751441478729248,4.254223823547363,0.9352738671004772,0.018104473393122773,0.9730945825576782,0.2068111485239331,0.041352685540914536,0.025920693688991605,0.045129736070521176,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",120,6.007534317439422,4.735780715942383,4.203982353210449,0.9334125518798828,0.021977893017409097,0.9878504164516926,0.21867047348370003,0.046145547181367874,0.029195089130342206,0.04452277102973312,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",130,6.50868840329349,4.755172252655029,4.161441326141357,0.9325875155627728,0.020734305555572926,0.9795742519199848,0.22937563416372683,0.045278117060661316,0.030547927874767837,0.04441404400859028,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",140,7.007126911194064,4.714611530303955,4.12645959854126,0.9339610300958157,0.02223850919984514,0.979720450937748,0.22053443735213848,0.04697050899267197,0.03105930562804079,0.044521413976326585,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",150,7.504776556044817,4.764510154724121,4.076414108276367,0.933015238493681,0.0222523906976638,0.9776702113449574,0.24739907086254323,0.045765675604343414,0.026749994968483095,0.043798297992907465,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",160,8.004113520029932,4.695204734802246,4.054452896118164,0.9306663349270821,0.027308358430277603,0.9781424030661583,0.2516442335895218,0.04595961794257164,0.031030000046986146,0.044637531042099,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",170,8.502123389043845,4.748479843139648,4.0313568115234375,0.9348172210156918,0.02289132997303212,0.9962353631854057,0.26951299307977866,0.0462932214140892,0.02431097704709011,0.04295699601061642,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",180,9.002213504398242,4.7318549156188965,3.9836113452911377,0.9328904561698437,0.02632444076502578,0.9841817170381546,0.2745699503258206,0.04418802261352539,0.024805148058258286,0.04316627804655582,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",190,9.502579970518127,4.709346771240234,3.9597697257995605,0.9318558089435101,0.02608947095814311,0.9906118102371693,0.25970807368801624,0.04758121818304062,0.026100953515283166,0.04313696804456413,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.0025345769245177507,7.116309642791748,7.130491256713867,0.9655163511633873,0.009975163118828251,2.0459831282496452,0.24076753406446522,0.25537871196866035,0.1363385908135309,0.04319131898228079,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.5065891987178475,5.797483921051025,5.807602405548096,0.9493725001811981,0.01671522024245663,1.3252091854810715,0.16486231309447244,0.10734732635319233,0.08861634889160144,0.04293569002766162,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,1.0088969345670193,5.480796813964844,5.400578498840332,0.9506535828113556,0.020146322054356843,1.2704985365271568,0.21803984034753324,0.09104331210255623,0.06868320561084734,0.043382004951126873,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,1.5090366683434695,5.202086448669434,5.078940391540527,0.9460167624056339,0.018876127249618645,1.1634666100144386,0.18176521201054943,0.07647017017006874,0.061988073626826096,0.04258521902374923,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,2.0110476934351027,5.016762733459473,4.853074550628662,0.9440327510237694,0.022467076632229542,1.1173832416534424,0.18672516535637365,0.06661731749773026,0.05424461938705551,0.045011222013272345,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,2.5163531184662133,4.882181644439697,4.70576286315918,0.9437546990811825,0.020349775997071978,1.0842599533498287,0.19929947287113875,0.0625654011964798,0.058974036759319136,0.044402141007594764,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,3.020619885530323,4.809563636779785,4.581893444061279,0.9414998702704906,0.022389810094079116,1.044861052185297,0.1758721032961647,0.05616626515984535,0.04637259597610012,0.04433622502256185,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",70,3.522138894535601,4.752256393432617,4.486223220825195,0.9402777515351772,0.021755378694869983,1.0243562534451485,0.19651751880727666,0.0529024675488472,0.04061585578538653,0.04388255102094263,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",80,4.024235474760644,4.724730968475342,4.413187503814697,0.9395137652754784,0.020836236418142744,1.005267832428217,0.18796328500492035,0.048085492104291916,0.03948408065798621,0.04375348205212504,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",90,4.524702328606509,4.703805446624756,4.333845615386963,0.9358834736049175,0.023295594633440924,0.9983054138720036,0.2132492620798464,0.04686872661113739,0.037372986570608085,0.04396146396175027,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",100,5.025767660699785,4.679365158081055,4.265767574310303,0.9345698840916157,0.02223582543800227,0.9715612642467022,0.19702076556570206,0.04200185835361481,0.034096555331095874,0.04401978000532836,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",110,5.529112975811586,4.631099224090576,4.228837013244629,0.937348011881113,0.026696884567490475,0.9817078039050102,0.2238640035945942,0.042791273444890976,0.034233081459928406,0.044095209101215005,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",120,6.031935725943185,4.664207458496094,4.173559665679932,0.9380481839179993,0.025097580917842904,0.9791541323065758,0.23714637750012324,0.04192293435335159,0.02978593165831508,0.04380869900342077,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",130,6.53509469202254,4.636982440948486,4.117088794708252,0.9335317350924015,0.02609542659939594,0.9574598930776119,0.24752696309913916,0.03740860894322395,0.02447965774302977,0.04403446801006794,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",140,7.03890773281455,4.672447681427002,4.108592987060547,0.9322223253548145,0.023003093091529467,0.9678420517593622,0.2496329306451453,0.04237515106797218,0.035046544988238344,0.04358473396860063,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.0025216620415449142,7.124147415161133,7.132974147796631,0.9666458144783974,0.011234883562812608,2.021990977227688,0.2739678764771269,0.25585650093853474,0.1396822129159546,0.04289732000324875,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.5040092240087688,5.867682456970215,5.839347839355469,0.9479662664234638,0.014915433827510788,1.351544439792633,0.18193812074351795,0.11330554448068142,0.08796629332488487,0.04420527699403465,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,1.004501647897996,5.583747863769531,5.536319255828857,0.9482420720160007,0.01690837180868383,1.2477962449193,0.2320724585778382,0.08868347853422165,0.07115208086180433,0.04362837504595518,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,1.5049638347700238,5.361588954925537,5.295768737792969,0.9434214197099209,0.02022271304527724,1.1644480526447296,0.22168258773973595,0.07235663384199142,0.06287825447207576,0.043663203017786145,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,2.0070383836282417,5.17194128036499,5.093134880065918,0.9415903612971306,0.014898127805576686,1.1187432073056698,0.19344182653485942,0.06078079342842102,0.051305142568915815,0.043618894065730274,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,2.5100867963628843,5.058724403381348,4.919888496398926,0.9414347223937511,0.019566615016150233,1.0768044628202915,0.1704349180187215,0.056735869497060776,0.05023415389672781,0.04395413293968886,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,3.010556369321421,4.985102653503418,4.786898612976074,0.9384792596101761,0.017895530089095724,1.0414590612053871,0.15863622245680908,0.05267248675227165,0.04942996483219727,0.043840451980941,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",70,3.5127256284467876,4.8699564933776855,4.673904895782471,0.9400634355843067,0.01884501128440391,1.0169268064200878,0.164958988976738,0.04613812267780304,0.04659338136449417,0.04367490403819829,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",80,4.015278681414202,4.800649166107178,4.590620040893555,0.9402777478098869,0.017825561521392214,1.015299804508686,0.1889116121870664,0.04499896615743637,0.039942961058235305,0.04395857499912381,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",90,4.519775729510002,4.780707836151123,4.5256781578063965,0.9384492188692093,0.02323114843709377,0.990018930286169,0.2130733127573021,0.04201008751988411,0.04194940077942136,0.0436774839181453,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",100,5.018822491518222,4.773574352264404,4.451084613800049,0.9386902116239071,0.02039430401643292,0.9948647990822792,0.22085736425484226,0.04361998289823532,0.042292105133021486,0.04392178601119667,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",110,5.517652232549153,4.719963073730469,4.393746852874756,0.9391485825181007,0.02288167049673262,0.9827101156115532,0.22475930691250245,0.041000064462423325,0.04158355885689304,0.04399755003396422,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",120,6.022650821600109,4.735243797302246,4.334283828735352,0.9382938742637634,0.02257074073850091,0.981395535171032,0.2142074569471373,0.04256357625126839,0.04206702504129578,0.043945756973698735,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",130,6.5262703446205705,4.754034519195557,4.2789812088012695,0.9378090538084507,0.024187005026637315,0.9650160372257233,0.22019437201536618,0.03990531712770462,0.03852115040058842,0.04384940303862095,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",140,7.0308052606415,4.795475006103516,4.243770122528076,0.9359456412494183,0.020961259345681896,0.9644396603107452,0.22499693134064955,0.039790697395801544,0.0371150479562778,0.04405230202246457,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.00245569902472198,7.115710735321045,7.136232376098633,0.9658213779330254,0.010545914401176344,2.0459123849868774,0.28373862066785116,0.2543877474963665,0.1458280189346463,0.04407025396358222,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.5046899267472327,5.742301940917969,5.733063697814941,0.9498887844383717,0.014928587461390468,1.3165071979165077,0.18942332821393143,0.10870654508471489,0.08891743487553615,0.04415156098548323,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,1.0057921760017052,5.329099655151367,5.287023544311523,0.9500431604683399,0.0122799829557265,1.2258345633745193,0.1803185550023418,0.08801576867699623,0.0720208458874629,0.04388350201770663,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,1.5067067911149934,5.077003479003906,4.997812271118164,0.9502584375441074,0.013612198377300301,1.1268614046275616,0.16137550079738874,0.06804423406720161,0.062057685124700196,0.044346125912852585,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,2.0075722652254626,4.898481845855713,4.761003494262695,0.9490355737507343,0.012346713828186572,1.0616908557713032,0.1564505376704832,0.055672239512205124,0.04668206898033412,0.0441817760001868,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,2.512534709298052,4.796127796173096,4.617593765258789,0.9447624348104,0.014151222457710687,1.0244998969137669,0.15097855910000899,0.04976631700992584,0.04118182797344948,0.044242372969165444,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,3.012469867360778,4.7386932373046875,4.510052680969238,0.941864900290966,0.013141271343123649,1.0035010166466236,0.1622478779297858,0.04625443369150162,0.03624405940987267,0.0441728780278936,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",70,3.512498358497396,4.671656131744385,4.42801570892334,0.9438491389155388,0.016166834773276677,1.0032655410468578,0.18916661677504149,0.04540051892399788,0.03192561962996956,0.04364970000460744,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",80,4.017794760409743,4.642346382141113,4.3617377281188965,0.9385710991919041,0.021649094683232218,0.9867711924016476,0.2101142872717152,0.04323994740843773,0.03182250847621968,0.04504759900737554,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",90,4.5204627614002675,4.634832859039307,4.2956366539001465,0.9408257007598877,0.015325013090354346,0.9633490703999996,0.20114534193958566,0.03868517652153969,0.028057319822916787,0.04340370302088559,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",100,5.018618862261064,4.595091342926025,4.229085445404053,0.9402468167245388,0.01878449904347545,0.9520111903548241,0.21360470092221412,0.037604574114084244,0.025928135000879524,0.04412322700954974,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",110,5.517264981288463,4.621916770935059,4.167410850524902,0.9374689161777496,0.018861146223302504,0.9558044373989105,0.21730211133611219,0.03918788954615593,0.026408510780448806,0.044186212006025016,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",120,6.0190856892149895,4.605934143066406,4.1186065673828125,0.935332827270031,0.018722149040502324,0.939342375844717,0.2258021702711768,0.03442353010177612,0.024081676310680717,0.044223924982361495,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",130,6.520621854113415,4.581820011138916,4.086498260498047,0.9379926510155201,0.02125083872674993,0.9515322558581829,0.239289120431241,0.036157287657260895,0.023275815707955165,0.043853667099028826,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",140,7.023110208217986,4.601805210113525,4.029392242431641,0.9356087855994701,0.021032437183630414,0.9347380958497524,0.23759140893054703,0.034917835146188736,0.023786813017648744,0.04380130104254931,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",150,7.524347261060029,4.560851573944092,3.9977867603302,0.9367988593876362,0.02033493348702925,0.9288693889975548,0.23425732246021716,0.033822230994701385,0.023624850127815993,0.04434298898559064,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",160,8.025005341041833,4.554646968841553,3.9630682468414307,0.9341477602720261,0.023885191254147977,0.9186458177864552,0.23023424787256927,0.03268837183713913,0.02725492612835238,0.04417558992281556,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",170,8.523888343013823,4.57383918762207,3.9313902854919434,0.9363129176199436,0.020936871432086477,0.928246159106493,0.2428245100273929,0.03388088196516037,0.026329088268477376,0.04336863802745938,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",180,9.022266625077464,4.603286266326904,3.905123233795166,0.9346911646425724,0.022727720455523338,0.9213952422142029,0.24034260214814615,0.03317176550626755,0.022282659646327446,0.04353805200662464,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",190,9.525772110093385,4.6550984382629395,3.8828718662261963,0.9332267120480537,0.0222168668904291,0.916921816766262,0.23784544410728908,0.03180771321058273,0.020458366379770353,0.043626565020531416,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.0025135900359600782,7.132724761962891,7.158044338226318,0.9657300636172295,0.010575737559876126,2.080195002257824,0.27103709950856725,0.25351613387465477,0.14527760994823977,0.044336502091027796,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.508206352009438,5.7883501052856445,5.778180122375488,0.9544361606240273,0.012897297712528956,1.3447436392307281,0.17999533023087003,0.10840953886508942,0.0822255137240427,0.04395297099836171,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,1.0085328361019492,5.558298587799072,5.490540504455566,0.9482733011245728,0.01543674881231337,1.2461481988430023,0.21992998390615073,0.09152961149811745,0.0751163362576322,0.04372445901390165,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,1.510848893900402,5.317000865936279,5.223795413970947,0.9478477388620377,0.016156995867176078,1.1872736252844334,0.2523778552077517,0.08035053685307503,0.06534798411324409,0.043303232989273965,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,2.01164129585959,5.067689418792725,5.00333309173584,0.941068708896637,0.019240189101564446,1.1078636012971401,0.20598247704318207,0.06445218250155449,0.051408472450966865,0.0433136869687587,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,2.5117695798398927,4.9223222732543945,4.840272426605225,0.940582774579525,0.018815193333203903,1.054474152624607,0.19830478430379492,0.056695982813835144,0.047300698975368906,0.04368428501766175,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,3.013496815110557,4.850532531738281,4.713756084442139,0.9411035999655724,0.021527316474847694,1.0476100258529186,0.20988562529515045,0.05428815260529518,0.04514187311338349,0.04363727103918791,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",70,3.5124506993452087,4.751358509063721,4.6039605140686035,0.9378664493560791,0.02199023297854126,1.0214730836451054,0.19572808650799134,0.049390584230422974,0.039933476222527284,0.04279735288582742,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",80,4.015532151446678,4.702990531921387,4.514185905456543,0.9347832314670086,0.017642758939065868,0.9976939111948013,0.20305671916489818,0.04564393684267998,0.037820721929880424,0.04337685089558363,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",90,4.518105476978235,4.6441802978515625,4.438299179077148,0.9373183362185955,0.0242264432063479,0.9919172450900078,0.18634164382425591,0.04637228325009346,0.03777406546932121,0.04370041214860976,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",100,5.019596717902459,4.630678176879883,4.365931987762451,0.9356408379971981,0.02174418252951932,0.9768001958727837,0.19869679397755685,0.04182697460055351,0.03431167229787227,0.043735263869166374,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",110,5.521773413871415,4.615059852600098,4.312839508056641,0.9337465725839138,0.023751560054805977,0.9752845205366611,0.21061198251703347,0.041046980768442154,0.03481104083695801,0.04276347509585321,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",120,6.044973331154324,4.572662353515625,4.246123790740967,0.9369209632277489,0.020480172053401118,0.9515166021883488,0.20506993672150345,0.038653384894132614,0.030546796829607675,0.04522862797603011,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",130,6.544856538181193,4.5472025871276855,4.225205421447754,0.938294917345047,0.018688155306006128,0.945818867534399,0.20970183800123798,0.035144973546266556,0.024295353746641768,0.04384193988516927,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",140,7.053117813891731,4.56566047668457,4.176972389221191,0.932222705334425,0.02697742888847968,0.9461845457553864,0.22037796482150251,0.036834511905908585,0.027431996583363257,0.043642759090289474,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",150,7.560528577887453,4.54658317565918,4.135645866394043,0.9358847439289093,0.023528083943719316,0.9408606141805649,0.22744994757719977,0.035204626619815826,0.028026684148253055,0.04378625983372331,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",160,8.058706067851745,4.536214828491211,4.113731384277344,0.9349412694573402,0.02220188420820294,0.9504917897284031,0.22794857226549983,0.037028051912784576,0.03174387843276632,0.044035612838342786,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",170,8.563067855197005,4.514059066772461,4.078980922698975,0.930700771510601,0.024395962931593376,0.9389517977833748,0.2316745140487366,0.03552136570215225,0.029070573128925494,0.04352907999418676,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",180,9.061943267122842,4.541333198547363,4.054754734039307,0.9341429173946381,0.021617164402976247,0.9324195235967636,0.2345743268411261,0.035170312970876694,0.027842428072949055,0.04361379914917052,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",190,9.566915984847583,4.531528949737549,4.042229652404785,0.9299339614808559,0.024530967736559064,0.9471168741583824,0.2417873527766457,0.03822560980916023,0.03257210094636691,0.04297826089896262,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",200,10.066522231441922,4.5374274253845215,4.010560512542725,0.928894393146038,0.027822104327609332,0.9489862993359566,0.25204906482100203,0.038084469735622406,0.03237423011296289,0.04398988303728402,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.002600469160825014,7.1361284255981445,7.158115386962891,0.9656690843403339,0.010340193908665253,1.9905773177742958,0.26242896749744804,0.2531488239765167,0.14026553494679844,0.04353264789097011,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.5068447839003056,5.8033528327941895,5.777364730834961,0.9469935595989227,0.016558024595065585,1.2972217798233032,0.14903911616870827,0.09970275312662125,0.07696322079450357,0.04377465485595167,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,1.0060254477430135,5.519011974334717,5.456113815307617,0.9483671486377716,0.01538479053448637,1.2257257401943207,0.17213183639521726,0.08259479701519012,0.06730760175362663,0.04369543888606131,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,1.5089578528422862,5.179145336151123,5.129893779754639,0.9420461095869541,0.018596978756131824,1.1489197984337807,0.1963019679939181,0.06667626276612282,0.05489641472089887,0.044267429038882256,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,2.0093573566991836,4.9322991371154785,4.874673366546631,0.9410134740173817,0.01885788145408384,1.0991546511650085,0.21626612308391532,0.061528999358415604,0.05183962578593991,0.04410212906077504,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,2.511845274362713,4.798073768615723,4.700992107391357,0.9396382458508015,0.018666904482115023,1.0419523976743221,0.2211794042357444,0.05131274089217186,0.04221575983394015,0.04371393006294966,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,3.012843707576394,4.73874568939209,4.5828471183776855,0.9387214556336403,0.01702099714015173,1.0143233984708786,0.22751127264781482,0.0498054139316082,0.043675695930126816,0.043580166064202785,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",70,3.512040771311149,4.643218994140625,4.482247352600098,0.9378393106162548,0.017704859439529028,0.9833452552556992,0.2054031645516846,0.04297877475619316,0.03648147567510444,0.04429212701506913,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",80,4.014380200998858,4.633306980133057,4.416942596435547,0.9382939487695694,0.02011906118572871,0.9684940092265606,0.2422379419357016,0.036897480487823486,0.031662926727396394,0.04415756487287581,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",90,4.518910210812464,4.644559383392334,4.356176376342773,0.9346008226275444,0.020685947597322403,0.9520414844155312,0.24200991190396431,0.037745699286460876,0.03922942322410824,0.044344607973471284,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",100,5.020496541168541,4.631154537200928,4.295705795288086,0.9312136247754097,0.021292441529318237,0.9425919763743877,0.2169599434674827,0.03578905016183853,0.029333976033425208,0.043817318975925446,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",110,5.520504737040028,4.656297206878662,4.2346978187561035,0.9295065328478813,0.02207066928109646,0.9077800512313843,0.20489941460247998,0.03097975254058838,0.026510483897217753,0.044010112062096596,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",120,6.0251313203480095,4.537166595458984,4.1859917640686035,0.9288050904870033,0.023593783681403607,0.9328717365860939,0.22045156918939746,0.033674437552690506,0.025595912540656834,0.044069505063816905,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",130,6.5266601596958935,4.53407621383667,4.1543474197387695,0.932830523699522,0.020311004034148046,0.9179699122905731,0.215087109408632,0.029915273189544678,0.025097440872206,0.04426156799308956,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",140,7.025857658823952,4.550731182098389,4.096573829650879,0.9292315393686295,0.02148243483888831,0.9059556946158409,0.21894640827078657,0.028991378843784332,0.020897328144151708,0.04397953883744776,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",150,7.5301331975497305,4.518043041229248,4.069060325622559,0.9255690537393093,0.02659335015000278,0.9069758839905262,0.21707295064294183,0.03108842298388481,0.02550678849340356,0.04430783097632229,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",160,8.03122521028854,4.501230716705322,4.028301239013672,0.9248645566403866,0.026991655797051924,0.9097772724926472,0.21891658980990034,0.030730530619621277,0.022864767465505142,0.04382624407298863,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",170,8.533754348289222,4.534463882446289,3.9964115619659424,0.9282891750335693,0.024572232585919524,0.9005121849477291,0.2141348044233804,0.02867448702454567,0.021257117264657652,0.04390348796732724,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",180,9.035267011262476,4.5580854415893555,3.975651264190674,0.9255668222904205,0.02393121768309631,0.900721937417984,0.20924854714886568,0.030111417174339294,0.025082848663884726,0.044409736059606075,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",190,9.536019052611664,4.516069412231445,3.9498889446258545,0.9297222532331944,0.02223712581916159,0.915385827422142,0.21213237421385722,0.03251303359866142,0.024915753040981568,0.04390608589164913,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.002633539028465748,7.119546413421631,7.152257919311523,0.9667355641722679,0.011544715197522743,2.061239942908287,0.2629874452174262,0.2568993028253317,0.13868409928350617,0.04372394806705415,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.5009535891003907,5.746977806091309,5.739247798919678,0.9508964419364929,0.013466963694411001,1.3489065319299698,0.17125955148388491,0.10960423201322556,0.0799687684266769,0.04287999705411494,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,1.0026286579668522,5.384768962860107,5.3190083503723145,0.945646058768034,0.015446664452207529,1.2045834586024284,0.16353760790097532,0.08133600279688835,0.06222214856375938,0.04295059200376272,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,1.502495476976037,5.072686195373535,4.991022109985352,0.9419562891125679,0.01794244526858581,1.0897680148482323,0.15552488564849534,0.062222354114055634,0.05117396384741266,0.04167451802641153,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,2.001023100921884,4.8884429931640625,4.779426097869873,0.9414369538426399,0.01745890654834891,1.0331946723163128,0.14280308913075562,0.04989418387413025,0.0470379042896894,0.04293305007740855,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,2.4994175718165934,4.840264320373535,4.636833667755127,0.93814292922616,0.01685383064302863,1.002735361456871,0.15993517276605684,0.049170996993780136,0.04687799376359172,0.04277048911899328,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,2.9997060706373304,4.709950923919678,4.503993988037109,0.9363395199179649,0.018640717179423335,0.9885753430426121,0.1644623272179433,0.04692982882261276,0.044617756782470715,0.04368104902096093,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",70,3.4979197159409523,4.679097652435303,4.41961669921875,0.9330785162746906,0.025347148041504907,0.9698343127965927,0.17578127531491222,0.04282115772366524,0.037175084171591564,0.043649788945913315,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",80,3.9968409300781786,4.601276874542236,4.327716827392578,0.9337794445455074,0.023284357261961954,0.9688777700066566,0.1872753599009891,0.04169921204447746,0.03955550310578297,0.04291989700868726,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",90,4.495205882238224,4.649994373321533,4.291510581970215,0.93789491802454,0.02306231737906844,0.9597499594092369,0.1941984367880403,0.04003572091460228,0.032441382029403366,0.04366031102836132,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",100,4.99399198894389,4.547727108001709,4.2026166915893555,0.9359777718782425,0.025328343821495403,0.9553426764905453,0.18643180026439798,0.03990195691585541,0.03409549667050646,0.04331978992559016,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",110,5.490266352891922,4.582202911376953,4.160947322845459,0.9340200684964657,0.024364508798602642,0.9451792910695076,0.19220598857471297,0.038841236382722855,0.03635730733110909,0.04371047904714942,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",120,5.986923637101427,4.5488505363464355,4.1017165184021,0.9324345476925373,0.026386152105801808,0.939590148627758,0.22336313325160498,0.03614729642868042,0.02964637309622621,0.04354961193166673,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",130,6.482966441195458,4.557779788970947,4.056117534637451,0.9306336045265198,0.02690891024027355,0.9417603388428688,0.21189617999451554,0.03617721050977707,0.028578006822888972,0.04347414313815534,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.002625886118039489,7.143449783325195,7.16644811630249,0.9657599553465843,0.010032972447936385,2.0072768107056618,0.25325067507905513,0.2587429415434599,0.14370560943425248,0.04447148204781115,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.5058727429714054,5.801052570343018,5.794381618499756,0.9506248086690903,0.015536653353983784,1.3411074578762054,0.15167887909394095,0.1045096106827259,0.08864383126243015,0.04364933702163398,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,1.004017699509859,5.4306793212890625,5.338429927825928,0.9457702450454235,0.014968467193273901,1.185160081833601,0.14460952838137062,0.08045155927538872,0.0687372096353179,0.043826665030792356,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,1.503688452532515,5.176697731018066,5.046415328979492,0.9443664699792862,0.009897380278281827,1.1007024720311165,0.12745295550318284,0.06376299262046814,0.055979138190294764,0.043980896938592196,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,2.0030285716056824,5.055909633636475,4.8603515625,0.9412563368678093,0.016587274108020657,1.0692630298435688,0.14892282100612825,0.057730838656425476,0.04801965924196142,0.04346881015226245,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,2.5034539646003395,4.950560092926025,4.7055888175964355,0.9390879049897194,0.01710205618287587,1.0447512716054916,0.15289346366252468,0.054904915392398834,0.04603878371773465,0.04357191198505461,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,3.003121705260128,4.924095630645752,4.601532936096191,0.9395788423717022,0.016304407259933425,1.0335024483501911,0.18179411216499444,0.05584225058555603,0.0504792812980038,0.04322870308533311,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",70,3.5031512032728642,4.840635776519775,4.501957416534424,0.9376243352890015,0.01944305609594208,0.9990586824715137,0.1852443447839043,0.048084553331136703,0.040599629271143335,0.043888597982004285,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",80,4.004874229663983,4.810042381286621,4.416120529174805,0.9367693327367306,0.018849380915325575,0.9856417924165726,0.18541356034835957,0.0452982522547245,0.040965821271096825,0.043111477978527546,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",90,4.507207585731521,4.744573593139648,4.360441207885742,0.9368596859276295,0.018844895251782817,0.9889268130064011,0.18773122491448777,0.04381350800395012,0.0347236117712037,0.04414134193211794,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",100,5.005117094609886,4.745031833648682,4.289236068725586,0.9328941106796265,0.019687540813525795,0.9804663397371769,0.19102776236123092,0.04612203314900398,0.03852844560012106,0.0442551898304373,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",110,5.505938058253378,4.723448276519775,4.221432685852051,0.9333810955286026,0.017313644738365046,0.9779592491686344,0.1826997187047659,0.04352453351020813,0.03437343479477377,0.04413341381587088,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",120,6.007452288875356,4.689965724945068,4.172774314880371,0.9361275285482407,0.02059001193144083,0.9662647694349289,0.19451736804143324,0.040297411382198334,0.031734231826285667,0.04426964605227113,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",130,6.509200643049553,4.750404357910156,4.129489898681641,0.9337170496582985,0.02313193052347812,0.9591059461236,0.18944875473190814,0.04179786145687103,0.034613985414743004,0.043071565916761756,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",140,7.011207324452698,4.746504783630371,4.1020283699035645,0.9326493144035339,0.020594024871543378,0.9621846079826355,0.1990586955318448,0.04225267842411995,0.03973255029969084,0.0439765730407089,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",150,7.516959275351837,4.728160858154297,4.074657440185547,0.9348750710487366,0.024311451599932653,0.9756276980042458,0.20680984169641572,0.0452243871986866,0.03944972819333874,0.04426726512610912,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.0064720220398157835,7.223785400390625,7.245999813079834,0.9667973667383194,0.010565420120022562,1.915524661540985,0.23385305490705893,0.2493113074451685,0.1369009359548024,0.1236282919999212,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,1.2660161409294233,5.4463791847229,5.356385707855225,0.9442757442593575,0.01604873126146665,1.2229667082428932,0.15302958805460343,0.08925801888108253,0.07167468184520047,0.12888991297222674,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,2.5214510530931875,4.794045925140381,4.624770164489746,0.934387780725956,0.01951798916642858,1.0909643955528736,0.1656265027587511,0.06717977300286293,0.06399007421678811,0.12380689103156328,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,3.773233759100549,4.529352188110352,4.113335609436035,0.9307841062545776,0.022677052145079122,1.004566129297018,0.1431670444478376,0.054453618824481964,0.04952631056683564,0.12389443092979491,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,5.057182700955309,4.303781032562256,3.6450366973876953,0.9190366044640541,0.024412227255648202,0.949682280421257,0.1552845490519284,0.046314988285303116,0.04252436182662118,0.13029407907743007,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,6.302869754261337,4.31278133392334,3.3795371055603027,0.9139728806912899,0.02790984326107542,0.9071064591407776,0.15386437646234769,0.04111778736114502,0.03631921284490879,0.12328656599856913,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,7.554763376363553,4.36143159866333,3.210824966430664,0.9078955985605717,0.02896074863621048,0.8968531899154186,0.16306715097129668,0.04302837699651718,0.03904466169355213,0.12371930503286421,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",70,8.802569207502529,4.222838401794434,3.018923282623291,0.9063422866165638,0.026323050540561912,0.8994043655693531,0.18456958101819132,0.042751457542181015,0.03558556420088398,0.12150125706102699,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",80,10.051006686757319,4.429418563842773,2.8673973083496094,0.9137587919831276,0.029844311574429887,0.8976067639887333,0.21804408494979877,0.043341014534235,0.039615593919171875,0.12195880303625017,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",90,11.301754632848315,4.547019004821777,2.8885769844055176,0.9102805741131306,0.03127621021623428,0.8894203901290894,0.19681831979515185,0.04594830796122551,0.04213827246097061,0.12193803605623543,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",100,12.546883252682164,5.015732288360596,2.6316099166870117,0.9082024060189724,0.029944349892709842,0.8953005708754063,0.22919971274118275,0.0460033044219017,0.04174555981511678,0.13111258996650577,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.005143804010003805,7.156919479370117,7.203100204467773,0.9679570943117142,0.010060308639412261,2.017511621117592,0.28232370435599896,0.2531901467591524,0.13662667313483598,0.1310948230093345,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,1.3062884679529816,5.421448230743408,5.340440273284912,0.9427808001637459,0.018951636405500075,1.2054190784692764,0.18683687995821144,0.07738674432039261,0.05950596534791884,0.13153317605610937,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,2.6050590567756444,4.740949630737305,4.624813556671143,0.9399098791182041,0.02006485931138748,1.071096919476986,0.21281730233133717,0.054939087480306625,0.04715453389673393,0.12961345398798585,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,3.9027136834338307,4.583536148071289,4.208078384399414,0.935882207006216,0.01940823030386906,1.0040963031351566,0.19587317741198754,0.04655170440673828,0.04104387906742847,0.13171154400333762,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,5.195022611529566,4.620460033416748,3.891770362854004,0.9330132938921452,0.021637719961981713,0.9819345772266388,0.2186421772336795,0.04481237754225731,0.043266063573562115,0.13159228500444442,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,6.485093278461136,4.615762710571289,3.6593587398529053,0.9239513352513313,0.023551648822816632,0.9650765135884285,0.22714589891624065,0.0466880202293396,0.04373642464224845,0.13259349891450256,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,7.7871311496710405,4.6922101974487305,3.51574444770813,0.9264543205499649,0.021203510116333486,0.9318291805684566,0.22728583874068503,0.04015972465276718,0.04451368273216625,0.135143127059564,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.006333795958198607,7.193406105041504,7.241690158843994,0.9661870822310448,0.010574914988019279,2.084733061492443,0.3175335117602668,0.2555733993649483,0.15189233967803029,0.13348685891833156,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,1.3037556649651378,5.389644622802734,5.299423694610596,0.945892047137022,0.018210111228777064,1.1957289315760136,0.207493951496373,0.07759013772010803,0.061140901628252434,0.1312844529747963,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,2.6224886558484286,4.887322902679443,4.65023136138916,0.9420782402157784,0.020788516012834015,1.0834062285721302,0.23914525747857737,0.05689828842878342,0.04201075698210582,0.13436006009578705,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,3.927484289975837,4.626141548156738,4.180879592895508,0.9344776123762131,0.019500644671892307,0.9868437796831131,0.1855163663333244,0.04321581870317459,0.03720799369450312,0.13058728107716888,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,5.221136321080849,4.3371076583862305,3.8053884506225586,0.9256312288343906,0.02051265762887301,0.9355438463389874,0.17905459058867232,0.03752120956778526,0.029580216740795532,0.12966908002272248,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,6.517750650877133,4.330966472625732,3.4586198329925537,0.9210821278393269,0.02422796695242152,0.9369108788669109,0.17583950279835434,0.04263027012348175,0.037189078900641916,0.13096497603692114,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,7.83453524671495,4.372443199157715,3.2813310623168945,0.919740591198206,0.023410054442947033,0.907453428953886,0.1874359117949037,0.03576744347810745,0.026659980769357213,0.14109383895993233,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",70,9.1348257814534,4.517110347747803,3.138044834136963,0.9182124733924866,0.020238509957531455,0.9037161730229855,0.17689009825068097,0.03615718334913254,0.024288472809919352,0.1315912869758904,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",80,10.4351177115459,4.437446594238281,3.000798225402832,0.9190060570836067,0.024845856843371587,0.8918172977864742,0.2001080476800917,0.036089926958084106,0.027274439864253774,0.13198994006961584,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.004601004999130964,7.4470038414001465,7.410826206207275,0.9670104831457138,0.012102718512400404,1.9328750893473625,0.26490058992867704,0.25844965502619743,0.1293427789033788,0.13077227596659213,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,1.2931798028293997,5.409799575805664,5.3542633056640625,0.9429933987557888,0.017666026857612954,1.21933014690876,0.17018973284475336,0.08266247436404228,0.06516554636119455,0.1318199160741642,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,2.5837531817378476,4.77207088470459,4.596982479095459,0.9330449774861336,0.020267933477727002,1.0818971619009972,0.19493736636133374,0.05967983230948448,0.05326636149745239,0.12993122299667448,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,3.8362339708255604,4.517875671386719,4.042593002319336,0.9250500164926052,0.026129760763483174,0.9965072683990002,0.2062260140963158,0.04489816352725029,0.04283461326060479,0.1269340260187164,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,5.139452898642048,4.380859851837158,3.670351028442383,0.9269145429134369,0.02687466117248491,1.0011973269283772,0.2336741193063012,0.04473423585295677,0.03661088916656579,0.13069484499283135,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,6.435778887360357,4.468667030334473,3.477351427078247,0.923101183027029,0.027554575094683443,0.9732178039848804,0.22927817509983178,0.046816907823085785,0.044367117534616154,0.13225395197514445,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,7.674822081113234,4.393873691558838,3.3373301029205322,0.9196802899241447,0.026773466753320915,0.9654744938015938,0.23375702480827693,0.04146423935890198,0.028693580192519823,0.12111588008701801,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",70,8.91507575684227,4.688445568084717,3.1782891750335693,0.9192518256604671,0.0275805528176765,0.9237259961664677,0.24091827891470405,0.03625565767288208,0.02629511932375599,0.11961071903351694,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.005152762983925641,7.22663688659668,7.257758140563965,0.9685075059533119,0.010479617581212656,1.9698176011443138,0.26732130564465845,0.25532173551619053,0.14797189261392843,0.13404741906560957,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,1.271350425784476,5.4753522872924805,5.372137069702148,0.9435763172805309,0.016480459008149564,1.1872559413313866,0.14734236933654885,0.07667255029082298,0.0600993202428684,0.125546958995983,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,2.52522443595808,4.912240028381348,4.695572853088379,0.9303316436707973,0.023201096332804042,1.0490171909332275,0.12868776569349302,0.05994775518774986,0.05073589364345824,0.12213217094540596,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,3.849334940663539,4.493138313293457,4.1749677658081055,0.931820847094059,0.019552339698268188,1.0280497632920742,0.14522017047011493,0.055081553757190704,0.047805941200202935,0.13065120694227517,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,5.1786694505717605,4.4042134284973145,3.831883192062378,0.9226396940648556,0.02165440672896786,0.9895196221768856,0.15283072549646,0.05661895126104355,0.056253442200375196,0.1349748659413308,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,6.5083130926359445,4.358863830566406,3.5136804580688477,0.917969174683094,0.023832533914748592,0.9542469009757042,0.14866970907484095,0.04881611838936806,0.04859614172699877,0.1373308360343799,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,7.75628789071925,4.390685558319092,3.3292877674102783,0.9178156815469265,0.026665666371160475,0.9616795666515827,0.19604813462615528,0.05405202880501747,0.05809682613832407,0.12644011597149074,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",70,9.011806977563538,4.610156059265137,3.141554594039917,0.9161091856658459,0.025513180984033434,0.966942273080349,0.1966859092245922,0.05679350346326828,0.06281842703955379,0.1259926490020007,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",80,10.345039252541028,4.48133659362793,2.9694290161132812,0.9171134904026985,0.025824223775714552,0.9570810720324516,0.1929020736635049,0.05393319949507713,0.05050961623612527,0.14349670405499637,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.006426898995414376,7.373189449310303,7.429987907409668,0.9691787585616112,0.009378634145681073,2.1243294775485992,0.3106216156474442,0.2672883830964565,0.15666094924311416,0.12316063093021512,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,1.250046122004278,5.269904136657715,5.242704391479492,0.9371323548257351,0.0215435709892832,1.1899956054985523,0.20589107177068972,0.07950982451438904,0.06346100023421147,0.12247053405735642,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,2.4878988378914073,4.4502482414245605,4.447965621948242,0.9333781860768795,0.02076391288453041,1.0677867457270622,0.19041944136990094,0.05788454785943031,0.051580336818812196,0.1236151100602001,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,3.729182575829327,4.310375213623047,4.054136276245117,0.9240764230489731,0.026788541687275246,1.0018951334059238,0.16750325776612357,0.05680076777935028,0.0514176334329999,0.1231518640415743,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,4.97510402277112,3.9873926639556885,3.6805546283721924,0.918916080147028,0.024742734428423042,0.9836954027414322,0.16804342952661236,0.0573609285056591,0.0626529432411805,0.1229089800035581,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,6.306918602786027,3.8823366165161133,3.4155139923095703,0.9122290387749672,0.030989833399211797,0.9328973814845085,0.17323543231972907,0.053056154400110245,0.05307665091604185,0.13784694008063525,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,7.591755117755383,3.9541749954223633,3.3210854530334473,0.9059480354189873,0.028469169059124964,0.9090774469077587,0.16719765375760942,0.049875203520059586,0.05641493022120103,0.13698423898313195,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",70,8.867108094622381,3.856656551361084,3.0811927318573,0.896575078368187,0.03385721948169565,0.8961157351732254,0.17892070347407787,0.0490524061024189,0.05419163221168506,0.1372066750191152,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",80,10.1154827515129,3.8352620601654053,2.807471990585327,0.8874867334961891,0.03278720762091336,0.8820408508181572,0.17132745951090878,0.05280438810586929,0.056335124642023844,0.12284667498897761,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",90,11.386026197578758,3.8365485668182373,2.660371780395508,0.8814066089689732,0.035394075397687394,0.9116953499615192,0.20446658468140003,0.059684522449970245,0.06150722172437135,0.12685692496597767,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",100,12.633796694572084,3.990649461746216,2.498016357421875,0.877808753401041,0.03909934486019192,0.8816522471606731,0.1985731791700371,0.05344792455434799,0.051027894262381024,0.12264683307148516,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",110,13.882359459646977,4.02473783493042,2.353503942489624,0.8696901276707649,0.03484831023117603,0.8782410137355328,0.20910674300556578,0.05715455859899521,0.05991724491412935,0.12251225498039275,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.004506921977736056,7.386490821838379,7.420776844024658,0.9671640321612358,0.012234599940300845,1.9214226752519608,0.2905134199714081,0.2542739175260067,0.1464260727737452,0.13531804189551622,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,1.3231329618720338,5.460224151611328,5.371434211730957,0.9486693441867828,0.014398655732680654,1.2215579450130463,0.19070555892639757,0.08775381743907928,0.06384129665920427,0.134241146966815,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,2.6367213148623705,4.919154167175293,4.703444004058838,0.9377459175884724,0.019683026487763208,1.0759298391640186,0.1794644746169243,0.06180155277252197,0.052840760411150996,0.13428850600030273,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,3.9573511749040335,4.500003337860107,4.13637638092041,0.926023468375206,0.0220106696455604,1.0130699835717678,0.1787300028309548,0.05373678728938103,0.045435785370612526,0.13863383093848825,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,5.277603541966528,4.446115493774414,3.790710687637329,0.9227938540279865,0.024159170132326272,0.9809309579432011,0.19382412005953234,0.04894757643342018,0.03844862235461288,0.13479286793153733,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,6.601734935189597,4.6196699142456055,3.5631015300750732,0.9218158461153507,0.027600308359376526,0.95602623000741,0.18542626786205138,0.04410586506128311,0.03399197599025055,0.14092381088994443,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,7.927503505954519,4.645652770996094,3.409440517425537,0.91754449903965,0.03226136723789485,0.9263617396354675,0.22086644828658514,0.03915800154209137,0.027545139660652425,0.13713476399425417,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",70,9.260163465864025,5.174102783203125,3.2163257598876953,0.9172689244151115,0.03163766174469816,0.9073771722614765,0.24500877386933165,0.03642503172159195,0.03194802604822651,0.14174721506424248,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.006874718004837632,7.214955806732178,7.23526668548584,0.968293197453022,0.009080634462561047,2.1007238626480103,0.2665165723471873,0.25895436480641365,0.13875831609443792,0.13552999508101493,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,1.3378950050100684,5.469055652618408,5.374948978424072,0.9431482963263988,0.019480364610375844,1.23378936201334,0.22234483701016045,0.0824119783937931,0.054380397747711945,0.13819836894981563,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,2.664498524973169,4.881351947784424,4.655710220336914,0.9341420195996761,0.022914467941755227,1.1178751401603222,0.24298028299838328,0.06058773025870323,0.048201532628733404,0.13803513906896114,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,3.993515139911324,4.592986583709717,4.0697736740112305,0.9266651272773743,0.022879911452688265,1.0325699150562286,0.23050987888953064,0.050331465899944305,0.03970688623081844,0.13676417199894786,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,5.330103320651688,4.505436420440674,3.653503179550171,0.9186119548976421,0.027644659655504916,0.9626211114227772,0.19175754739914358,0.04650323465466499,0.03755747758685072,0.13652631803415716,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,6.652976786834188,4.414055347442627,3.371253252029419,0.9068031758069992,0.03329597311009562,0.9292066060006618,0.2117055340866706,0.040298476815223694,0.03314900378442932,0.13793199905194342,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,7.973600991885178,4.421872138977051,3.0960843563079834,0.906130950897932,0.034399436993739374,0.8957124948501587,0.2038999164791777,0.03950439393520355,0.03129868987143155,0.13524303399026394,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",70,9.289394207065925,4.57697868347168,2.89725661277771,0.8930954448878765,0.04067165744127427,0.8521708995103836,0.1958416048487776,0.03205979987978935,0.02315130419146915,0.13849620590917766,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",80,10.603871037135832,4.520909309387207,2.7665598392486572,0.8973102904856205,0.0330629407110083,0.8377003222703934,0.19017609678126204,0.03668920695781708,0.03452385317191762,0.14402797305956483,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.004893096047453582,7.270867347717285,7.338106155395508,0.9654845967888832,0.011708940619215947,2.0154795572161674,0.2883834282059985,0.2541405241936445,0.14672252208472172,0.12017441005446017,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,1.2424727929756045,5.339860916137695,5.257294654846191,0.9428407326340675,0.017059530262721414,1.2144768983125687,0.23622672924663918,0.07950147241353989,0.05869169242874641,0.11811310204211622,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,2.4811097668716684,4.7025909423828125,4.522634983062744,0.9350287765264511,0.02090457204049237,1.1209091171622276,0.17069592398230596,0.0716608576476574,0.067485347871301,0.11842827592045069,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,3.720775725785643,4.486437797546387,3.9746556282043457,0.9339023642241955,0.019302574700978938,1.0452667623758316,0.19140092468906822,0.05895514786243439,0.050743796725784544,0.12029615801293403,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,4.955481861019507,4.353808879852295,3.6228063106536865,0.927339281886816,0.02329937251773523,1.0297961421310902,0.21767408202366795,0.056001268327236176,0.04187546834743459,0.12157246202696115,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,6.195427926024422,4.316719055175781,3.3884809017181396,0.9290152229368687,0.02623778178107515,0.995950311422348,0.22479176579437601,0.05054264888167381,0.037397096476003616,0.12072639295365661,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,7.43400745105464,4.547611236572266,3.2280478477478027,0.9295959025621414,0.022252602124666964,0.9950248934328556,0.2664288141463492,0.05100131034851074,0.034553115757480206,0.11781590199097991,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",70,8.672798324958421,4.574422836303711,3.113450527191162,0.9266078062355518,0.028136836506979852,1.0141725540161133,0.27850202780550937,0.053997140377759933,0.0322268571284815,0.11979421600699425,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",80,9.910293149878271,5.373210906982422,2.9818227291107178,0.9265449680387974,0.02732901181228747,0.9910320825874805,0.28250045163960047,0.05141501501202583,0.03300282325557815,0.12067331408616155,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.005753910983912647,7.181149482727051,7.213551998138428,0.9675908572971821,0.010098944851801767,2.0678929910063744,0.251587253622686,0.251965144649148,0.13506209374548628,0.12245525198522955,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,1.2417099761078134,5.304685592651367,5.238366603851318,0.9435445591807365,0.01857476371750595,1.201042853295803,0.21996104078063966,0.07866943255066872,0.050799353236368,0.11808462406042963,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,2.4788237041793764,4.786958694458008,4.563310623168945,0.9355454258620739,0.01748276756580765,1.0778843723237514,0.17822384700256652,0.05840972810983658,0.05009697744660905,0.12025893304962665,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,3.7209930514218286,4.52205228805542,4.176229476928711,0.9328294657170773,0.018592329148818875,0.9945984035730362,0.16763368835175183,0.04893171042203903,0.04877102095194563,0.11916056298650801,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,4.956896579824388,4.220110893249512,3.782336473464966,0.9250822179019451,0.022449455277357946,0.9629571624100208,0.18143891636150725,0.04493459314107895,0.041721437407400135,0.11942861799616367,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,6.194610111648217,4.0649590492248535,3.4777259826660156,0.9204388335347176,0.022523710227040583,0.9356542751193047,0.1678696617006792,0.04629535600543022,0.036665960073705484,0.11939109396189451,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,7.428071126691066,3.969465732574463,3.2769908905029297,0.9178777076303959,0.02089481396245687,0.9082261025905609,0.16703854563280926,0.0452272966504097,0.03655183895658493,0.11845639301463962,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",70,8.661875928635709,3.966503620147705,2.987543821334839,0.9068610221147537,0.031046127907992202,0.8938739709556103,0.1852888014785334,0.0460195355117321,0.036609386146253226,0.11915168794803321,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",80,9.901387041434646,3.9400956630706787,2.923126220703125,0.9086308814585209,0.028154163838819978,0.8786261342465878,0.1988750542271719,0.042587101459503174,0.029134977441402185,0.11805523803923279,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",90,11.141277234186418,4.003973960876465,2.667581796646118,0.9010343477129936,0.02793483705154291,0.8770994916558266,0.19191946842532365,0.046573154628276825,0.038422299447127675,0.11787846591323614,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",100,12.384683522162959,4.268207550048828,2.5929572582244873,0.8981965221464634,0.030917226194211465,0.861627884209156,0.21747011637600586,0.043257229030132294,0.02866743101887315,0.11986289906781167,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",110,13.623336165212095,4.168821334838867,2.4997568130493164,0.8943799547851086,0.03472108235325573,0.8836401924490929,0.2358534144834637,0.04792466759681702,0.032324771292522,0.12028703792020679,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.007744238944724202,7.5214457511901855,7.4939703941345215,0.9680481031537056,0.008835355860820947,2.133276768028736,0.27448279256696206,0.28401623852550983,0.12535707649862096,0.18774852098431438,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,1.9178418079391122,5.251077175140381,5.143151760101318,0.9400620199739933,0.021510974258596563,1.161885429173708,0.21572153632756672,0.07637828961014748,0.0569694312544449,0.18610359204467386,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,3.8344385131495073,4.433572292327881,4.087447166442871,0.9287703484296799,0.026601687506733872,1.0508428402245045,0.18150372516850657,0.05675378814339638,0.05204792773921899,0.187836205936037,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,5.748211903031915,3.978161096572876,3.326068639755249,0.9117451794445515,0.03010523262265708,1.0296069644391537,0.20671224185011491,0.05603039264678955,0.051578784596879805,0.19124526402447373,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,7.6601572561776266,4.092563629150391,3.0203464031219482,0.9121416695415974,0.033336747532403,0.9683803804218769,0.18215556831829843,0.05181393399834633,0.04328163488392724,0.1867266809567809,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,9.559710828354582,4.657101631164551,2.8350868225097656,0.9024996310472488,0.033840369309861756,0.9384835697710514,0.18659442449516384,0.049652792513370514,0.05146168190327999,0.18454709206707776,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,11.466326118214056,4.5163116455078125,2.4887166023254395,0.8966676518321037,0.036419594838478314,0.9001395180821419,0.16645565357764033,0.04519127309322357,0.04233668772778095,0.1845631810138002,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.006154119968414307,7.177927017211914,7.208149433135986,0.9666779451072216,0.01285582443805667,1.9879380762577057,0.30805376228399867,0.25456629879772663,0.1488711466814929,0.18395251710899174,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,1.9019908831687644,5.142783164978027,5.06233024597168,0.9406437501311302,0.019261585223106177,1.1587569303810596,0.17000407550850216,0.07526247948408127,0.060549155416192976,0.19245084503199905,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,3.793644444202073,4.076972007751465,3.761960029602051,0.9168090708553791,0.030290880724621837,1.1257866397500038,0.3375763573137179,0.05675802752375603,0.044857288780644024,0.18700763792730868,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,5.681039817165583,3.8737998008728027,3.232642412185669,0.9126897118985653,0.031028471823470846,1.067966740578413,0.2850684817687456,0.05311485752463341,0.048487239156006605,0.18617146101314574,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,7.585137850255705,4.021998405456543,2.898001194000244,0.904386218637228,0.03148533728245501,1.0347193777561188,0.29062623789952347,0.05395045503973961,0.050139424555642034,0.18695307499729097,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,9.497572216088884,4.570245265960693,2.6636857986450195,0.9063712805509567,0.027491949867891483,0.9948672614991665,0.22314989518791017,0.04994844272732735,0.03931167654637495,0.18771338101942092,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,11.391846984974109,5.0308451652526855,2.5157806873321533,0.8973987698554993,0.03213082771941835,0.9964758157730103,0.2828782073294975,0.04899806156754494,0.042162111369024376,0.18538393802009523,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.006605813978239894,7.416009426116943,7.441357135772705,0.966369416564703,0.012556347024205747,2.0339407697319984,0.2814849337739313,0.267949840053916,0.1459098962434709,0.18757687299512327,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,1.901300385943614,5.202291011810303,5.085083484649658,0.9404886215925217,0.018523266097289923,1.1553513295948505,0.19935029390260275,0.07180740311741829,0.05453209861240275,0.18695608398411423,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,3.797491871053353,4.288596153259277,4.018150806427002,0.9240112751722336,0.025248354160927973,1.0270237363874912,0.1848340271290722,0.05302466079592705,0.0490576716295029,0.18658207391854376,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,5.694054311839864,3.993497848510742,3.338303565979004,0.9054878018796444,0.03960486894717971,0.9729337729513645,0.20908549527093137,0.045183051377534866,0.04637916696406504,0.1849794170120731,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,7.590502885635942,3.979644298553467,2.9903793334960938,0.8977383822202682,0.03240678847223142,0.9703958854079247,0.21503074981518966,0.0425766222178936,0.03576045966757965,0.18761026998981833,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,9.486664956668392,4.191311836242676,2.6824350357055664,0.8997518494725227,0.025646995654478148,0.9410725384950638,0.25811180190461397,0.04181937873363495,0.04292986070012355,0.1854888959787786,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,11.381736581446603,4.210968017578125,2.550727367401123,0.8873629048466682,0.03161532883220468,0.9347490668296814,0.27058196367011805,0.037024300545454025,0.03447857414728476,0.18679353094194084,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",70,13.273233879473992,4.280418872833252,2.3072171211242676,0.8897724151611328,0.02681617431735512,0.9094206001609564,0.2786720832698321,0.03704368695616722,0.03029643006212491,0.18469856400042772,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.005998975946567953,7.218636512756348,7.23930549621582,0.9688733592629433,0.010130149007995142,2.0779180452227592,0.3065684222178275,0.26760521344840527,0.14288800060199977,0.1850366600556299,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,1.9043544287560508,5.1764984130859375,5.104836463928223,0.9401865005493164,0.01777048453287265,1.1691375635564327,0.1778049263169077,0.07322639971971512,0.05914794587668616,0.18380761297885329,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,3.790340438601561,4.592860221862793,4.215725898742676,0.9252303317189217,0.022422600652683266,1.0175108686089516,0.15937920896832164,0.05350777506828308,0.051119419618088725,0.1876713839592412,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,5.675597723806277,4.227401256561279,3.579462766647339,0.9209899231791496,0.02421317649883133,1.01076939702034,0.21563173045426356,0.048566095530986786,0.04581464510333607,0.18615170801058412,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,7.55825557152275,4.167660713195801,3.0710434913635254,0.9078361801803112,0.02666992361396835,0.9542149752378464,0.22738746224658724,0.04066023603081703,0.043460992225487015,0.1849137960234657,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,9.45387375052087,4.152390480041504,2.64133620262146,0.9036583937704563,0.026997071701276246,0.900140568614006,0.24297469763633292,0.03471934795379639,0.02550791368609849,0.1838969640666619,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,11.336754778632894,4.237327575683594,2.472651481628418,0.9041745997965336,0.030674204641924895,0.9024895206093788,0.26910663486936615,0.03984154388308525,0.04689700788431517,0.1843260150635615,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",70,13.221758218482137,4.78726863861084,2.1527485847473145,0.8920895718038082,0.030778120812870288,0.8630513288080692,0.2152619814595317,0.03617212921380997,0.02711514266869878,0.18456846405752003,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",80,15.11120952339843,5.589849948883057,2.0211193561553955,0.8923041075468063,0.033990159670591734,0.8463469333946705,0.23254810409274673,0.03469221293926239,0.025636566211175972,0.1850122770993039,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.006172889028675854,7.28122615814209,7.294007301330566,0.9678965583443642,0.008692346131497296,1.9982265159487724,0.25003275638195216,0.24488581530749798,0.1446980263093293,0.1855061190435663,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,1.9068176512373611,5.122416973114014,5.069519996643066,0.9416202530264854,0.018500348127271124,1.1751039065420628,0.19437688795238334,0.07526523992419243,0.060464482548101134,0.18718502297997475,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,3.8028728102799505,4.068846225738525,3.7401416301727295,0.9197691418230534,0.028425350851039727,1.0887600667774677,0.2731883070966961,0.06051056832075119,0.04764587985325803,0.18326928094029427,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,7.619987164624035,4.031265735626221,2.8112096786499023,0.9050293006002903,0.03217542982380282,1.0055588148534298,0.23601213670474588,0.05229876562952995,0.044591677811235075,0.1839776289416477,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,3.796208343002945,4.282249450683594,4.0139265060424805,0.9248994439840317,0.028013101559954876,6.092564765363932,19.020348934573157,0.06946664303541183,0.06110402406516674,0.19071629305835813,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.006361796986311674,7.376331806182861,7.3756561279296875,0.9682612232863903,0.011377382181809264,2.1583214700222015,0.23122090814308222,0.26538041047751904,0.12786927369374343,0.18746965506579727,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,1.9034339740173891,5.266102313995361,5.117325305938721,0.9413135088980198,0.01977533464700928,1.138245441019535,0.1726154335177751,0.06905572861433029,0.05515002979578661,0.18778266699519008,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,11.354955223039724,4.102197170257568,2.7884578704833984,0.9140637516975403,0.039418364542084756,0.9980690963566303,0.2925972713986244,0.055710483342409134,0.039306066414722174,0.1853548240615055,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,7.595356073812582,3.8634872436523438,2.8908095359802246,0.8994473442435265,0.03683188002188933,1.0499441884458065,0.24165591929881197,0.05131708085536957,0.04659869132589943,0.18517630803398788,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,5.702648053877056,3.8910646438598633,3.22575306892395,0.9064623042941093,0.03344802783624034,1.0978864207863808,0.31787579882071393,0.053827956318855286,0.04498726043263093,0.18483324605040252,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,7.583516437676735,4.017875671386719,2.889901638031006,0.9041741490364075,0.03159165367759473,1.028526946902275,0.3012032480200626,0.048337262123823166,0.04310155296291533,0.18414487200789154,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,3.803786930977367,4.489386558532715,4.067745208740234,0.9275815524160862,0.025680340018284436,1.0875911265611649,0.20121703469467853,0.05995403230190277,0.05539360029361153,0.18657659308519214,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.005030261003412306,7.244483947753906,7.248755931854248,0.9682015888392925,0.011388062537332929,2.0018474236130714,0.2385465080947913,0.24843444302678108,0.13682681171911276,0.16026834305375814,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,5.448374298168346,2.696681499481201,2.3557963371276855,0.8411879763007164,0.04609159978907486,0.8126741498708725,0.0898483942605858,0.044265490025281906,0.0352290526377674,0.15933192800730467,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,6.5327031122287735,2.617194890975952,2.215787410736084,0.8246432356536388,0.050384521281695814,0.8194501660764217,0.1301225592142209,0.05303049832582474,0.05680989356263973,0.15764556999783963,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",120,13.016831601969898,2.2614622116088867,1.5777881145477295,0.8127784132957458,0.051699745213336104,0.7189675271511078,0.12375635369674684,0.03560502082109451,0.03406390579144607,0.15370543708559126,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",170,18.4225008568028,2.1484146118164062,1.3501553535461426,0.7920270301401615,0.05986061982904433,0.7083788234740496,0.14009327951766598,0.03581084683537483,0.03449028267732139,0.15999778592959046,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,4.330879727145657,3.0158989429473877,2.605316400527954,0.8367890901863575,0.046067357968569804,0.7927583083510399,0.08450502727484083,0.04427352175116539,0.03622872927459169,0.1562206280650571,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,5.409584269975312,2.8006246089935303,2.3457632064819336,0.8379839956760406,0.0479406945558966,0.7679674029350281,0.1324201586167738,0.04440242424607277,0.03774915475922419,0.157811225974001,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",100,10.796977874124423,2.4222288131713867,1.59901762008667,0.7993488758802414,0.051823373194381496,0.7066168300807476,0.16664914862588165,0.036845527589321136,0.03564950878807395,0.15643667802214622,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",150,16.187350679188967,2.310753107070923,1.3794878721237183,0.807002954185009,0.03791766905502863,0.7408649288117886,0.20783431836925179,0.042631909251213074,0.04565753804167715,0.15420862403698266,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,4.3192791029578075,2.948713779449463,2.628626823425293,0.8581259325146675,0.054754852893550456,0.8396657034754753,0.15626386953137636,0.052227556705474854,0.05078300686942269,0.15642857202328742,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,5.403141010901891,2.658482313156128,2.326512336730957,0.8421297408640385,0.05753000581915494,0.8148746863007545,0.16723500496900742,0.048578087240457535,0.042324436817089174,0.1558134340448305,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",100,10.802552502718754,2.3484504222869873,1.6526820659637451,0.7949183806777,0.06030538011488061,0.6808880176395178,0.11322055004369733,0.028999365866184235,0.0235048791322661,0.15690199506934732,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,2.1534871987532824,3.640906572341919,3.3980443477630615,0.8809207454323769,0.05119818608305251,1.0113921537995338,0.14833128661644007,0.0786023810505867,0.06150755035874628,0.1585790270473808,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",70,7.535192946670577,2.459456205368042,1.9174145460128784,0.812194898724556,0.05200574370439414,0.7609992362558842,0.09027371391840742,0.039778515696525574,0.03410078151491688,0.15850954898633063,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",120,12.915630254661664,2.194993495941162,1.510810136795044,0.7917460724711418,0.05371341818598071,0.6612928174436092,0.10692541212169947,0.028960388153791428,0.031261616857330234,0.15802155598066747,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",140,15.073765005567111,2.198871612548828,1.3928248882293701,0.7901262007653713,0.04599906735072717,0.6740893498063087,0.1035904428465039,0.029596928507089615,0.029765998877209557,0.15505300997756422,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,3.2441343989921734,3.303802013397217,3.0123820304870605,0.8615701608359814,0.05832641719848852,0.8704605884850025,0.1405895293719115,0.05858771502971649,0.051824383691298125,0.15482101100496948,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",80,8.641211344744079,2.660806179046631,1.9618709087371826,0.8218380622565746,0.054181838435948704,0.7391858920454979,0.1012956133908769,0.03867911547422409,0.0276178432267376,0.15297701803501695,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",90,9.726320528774522,2.541940212249756,1.8738861083984375,0.8299209102988243,0.04755307492555008,0.7341786846518517,0.1080249099732284,0.037936948239803314,0.02786199722261848,0.1532707300502807,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",140,15.133262400981039,2.336139440536499,1.534929633140564,0.8011764362454414,0.05649906194285051,0.7266595996916294,0.11698803766475278,0.03849499300122261,0.026176089899867604,0.15222779801115394,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",200,21.636389084043913,2.321662664413452,1.221998691558838,0.7974569909274578,0.05206000008319031,0.719326563179493,0.10312115160028346,0.03735434263944626,0.029180057984332233,0.15350134996697307,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,4.326472648768686,3.049983024597168,2.741673469543457,0.8702360838651657,0.034610494989097663,0.9098346419632435,0.14750653546947687,0.05864954739809036,0.03927402476887575,0.14918518206104636,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,5.40276273782365,2.8298041820526123,2.4001076221466064,0.843811571598053,0.047104138309091874,0.8504537045955658,0.15042377850920816,0.05576677247881889,0.0456286798736911,0.15372153604403138,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",100,10.800954148755409,2.272247791290283,1.7016733884811401,0.8217476457357407,0.043597271982908074,0.7298699133098125,0.1064349749944011,0.03738474100828171,0.0363112483666721,0.15226345800329,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",150,16.201418864773586,2.142589807510376,1.3633244037628174,0.8152144514024258,0.04535956673212063,0.7064574807882309,0.12849388416472418,0.035538457334041595,0.03916923144275389,0.1526546130189672,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.003171234973706305,7.3140668869018555,7.306934833526611,0.968690063804388,0.009504864849881054,2.081448845565319,0.2567388023355457,0.25891800969839096,0.14105452929044143,0.1503354860469699,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,2.1605981750180945,3.7627835273742676,3.493711233139038,0.8856511227786541,0.03704952113880632,0.9858757890760899,0.14965626923884878,0.0720214769244194,0.05526733157496378,0.15445870801340789,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",70,7.564332608832046,2.437706470489502,1.944166660308838,0.8138155937194824,0.04840443619231574,0.7734642177820206,0.1218665952725628,0.04708445072174072,0.04138482341539938,0.1496063880622387,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",120,12.98085314326454,2.198314905166626,1.4792132377624512,0.8019442856311798,0.05861206923281027,0.7288737669587135,0.13423323716364868,0.03729219362139702,0.024271730051279878,0.15480812604073435,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.0033029239857569337,7.292778968811035,7.341848850250244,0.9657615944743156,0.011374788612455389,2.0091880559921265,0.2870362018673458,0.25819907523691654,0.13702615619742559,0.15246110793668777,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,1.079416934051551,4.533519268035889,4.41342306137085,0.9062183983623981,0.03483790321812322,1.1376638114452362,0.16055574451691473,0.09586821123957634,0.07505182356746594,0.15361449401825666,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,6.4517252473160625,2.5184261798858643,2.1364262104034424,0.8445755615830421,0.05315277780108237,0.7802591100335121,0.10468182293804892,0.04248034209012985,0.030285626308844422,0.15840644598938525,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",120,12.909740282106213,2.018817663192749,1.4790947437286377,0.8019411489367485,0.04619780855727499,0.7186128310859203,0.1057120638724495,0.0386175774037838,0.03356684484345972,0.15459836495574564,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",170,18.287909320904873,1.9670331478118896,1.2421725988388062,0.7799970991909504,0.04843859853384996,0.7107563130557537,0.142876604543028,0.03897780552506447,0.03569689213711324,0.1537624888587743,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.0032320490572601557,7.259212970733643,7.284676551818848,0.9635623767971992,0.01162122415096778,2.0032094717025757,0.25618514479079113,0.2538514733314514,0.1276627135499725,0.15438809292390943,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,5.365427762037143,2.6609582901000977,2.2920007705688477,0.8325773738324642,0.05853560230405718,0.7597793564200401,0.14535648429870718,0.03900580108165741,0.03131887124585404,0.15576267917640507,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",100,10.735822924412787,2.1624417304992676,1.558586835861206,0.8136633038520813,0.04217793270865813,0.7275273576378822,0.178535122941451,0.03460244834423065,0.02064110920296326,0.14885992207564414,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",150,16.107237588614225,2.0714504718780518,1.2039061784744263,0.8018464893102646,0.042307769741975976,0.6943340096622705,0.17981970235243055,0.0320911705493927,0.01954746689950814,0.14900935185141861,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.003136491170153022,7.2820892333984375,7.271611213684082,0.9645402207970619,0.01230946655099513,1.9608673304319382,0.29913733475692594,0.25095411390066147,0.15373184800382464,0.15111457486636937,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,5.3850319867488,2.9383368492126465,2.5730743408203125,0.8432911857962608,0.06295651264797508,0.8546996489167213,0.15124703109567791,0.05739458650350571,0.05547123823481409,0.14852545596659184,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",100,10.75060098269023,2.5311169624328613,1.9045703411102295,0.8144524805247784,0.06078290776938375,0.7659181877970695,0.1377963135207998,0.047600485384464264,0.05215103224926758,0.14904279098846018,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",150,16.115103913238272,2.3428642749786377,1.5964789390563965,0.804383460432291,0.059107429680017746,0.7516671977937222,0.10041508657151595,0.04403162747621536,0.038424653033926184,0.15309608005918562,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",160,17.19406452216208,2.3891665935516357,1.5505414009094238,0.8056926503777504,0.05624481951885757,0.7619593590497971,0.10941352833552158,0.044102683663368225,0.036214678182068936,0.14925400097854435,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,3.0053185740252957,3.740633249282837,3.4721884727478027,0.8746057450771332,0.050928497914961415,0.9822103418409824,0.15531543856799193,0.07398931682109833,0.060106464405021036,0.47987734293565154,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.00576159602496773,8.269892692565918,8.16054916381836,0.9725047722458839,0.010162180156827901,2.35601469874382,0.41235884095279185,0.3351765498518944,0.1382815914157693,0.4707379810279235,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,14.880595842958428,2.9152190685272217,1.386217474937439,0.8162837699055672,0.04985050772892961,0.7591492421925068,0.1035354504293069,0.0451497845351696,0.041841239884793074,0.4864781469805166,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,17.855299423914403,2.882648229598999,1.1767429113388062,0.8174707069993019,0.052732419212708895,0.7534991949796677,0.12474956408083118,0.044826362282037735,0.04141258299884765,0.48955135501455516,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,8.922746904543601,2.9055991172790527,2.004554033279419,0.8293777965009212,0.06068065668269351,0.8143424913287163,0.15273784549674227,0.053466103971004486,0.04560591488921215,0.4691823839675635,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.006197932991199195,7.183951377868652,7.227557182312012,0.9644175209105015,0.014177992259359081,2.0244882479310036,0.278839941016509,0.2536455523222685,0.1420332950168404,0.4842494719196111,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,17.894646164844744,2.6338119506835938,1.1377846002578735,0.8148216940462589,0.05184785611488308,0.7766920700669289,0.1395062478898916,0.046964533627033234,0.040595728493692314,0.4766329430276528,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",70,20.87767773878295,2.786747932434082,1.0685410499572754,0.8082837462425232,0.040273004128896984,0.7666406445205212,0.1771723452376804,0.04834778606891632,0.04714514915903478,0.474332690006122,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,5.963436053832993,3.363828182220459,2.7048263549804688,0.8327368274331093,0.04797164169715351,0.8957074582576752,0.14044057154069226,0.06670570001006126,0.0551292554935337,0.4938628950621933,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",70,20.868535659858026,3.0128424167633057,1.1809444427490234,0.8192460797727108,0.042609029397028014,0.7982376366853714,0.12917485239851031,0.049839578568935394,0.045698172323441186,0.47398902801796794,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,8.934805330238305,3.16788387298584,1.9840279817581177,0.8242504000663757,0.05748334161409297,0.8166247121989727,0.12768406362284962,0.049610309302806854,0.04957524481902275,0.4731719580013305,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,11.91451893816702,3.194671869277954,1.760404348373413,0.8222963511943817,0.04367107219250605,0.7809238247573376,0.14068868590018824,0.04867479205131531,0.05181172276508217,0.4976424209307879,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.005974382045678794,7.347845077514648,7.331674098968506,0.9670125618577003,0.011043215565111333,2.119510918855667,0.26881972488360945,0.2717560902237892,0.12897053698219477,0.4861724360380322,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,14.881299024214968,2.76358962059021,1.4630640745162964,0.8009969294071198,0.053227019833550236,0.7093192487955093,0.10260103105360584,0.040239956229925156,0.04150003027279892,0.4790478489594534,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,5.954807028989308,3.2597622871398926,2.514733076095581,0.835328433662653,0.05475845781376321,0.8538582995533943,0.1229276858559942,0.05829336494207382,0.05429931104588105,0.48127026308793575,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,8.928983489051461,2.993644952774048,2.0144169330596924,0.8299885168671608,0.05625604022870205,0.8343578986823559,0.14181413000332876,0.05919811502099037,0.0510398886961055,0.4832106289686635,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",80,23.805510028381832,3.1473441123962402,0.8836511373519897,0.8056632727384567,0.053655517780782806,0.7382027730345726,0.12469948586861941,0.042019497603178024,0.039977285426886375,0.4791953740641475,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,11.93767330457922,2.877521514892578,1.6940739154815674,0.8243408165872097,0.04399037474829209,0.7829765193164349,0.11630374577883207,0.04789360612630844,0.04199551480452407,0.49882332200650126,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,5.959455587319098,3.1627914905548096,2.687501907348633,0.8594677001237869,0.04237407569313901,0.9264227263629436,0.15244192329990267,0.06723149865865707,0.05261718561708777,0.4802650830242783,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,8.92827143741306,2.9687581062316895,2.1414663791656494,0.8348090164363384,0.049297073476882114,0.8319799229502678,0.15277490615409545,0.0592825710773468,0.054431006054119206,0.4640126480953768,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",80,23.805163963348605,3.0259523391723633,1.0884970426559448,0.8134481832385063,0.06279386949604653,0.7559675239026546,0.18398024263138568,0.04852985590696335,0.05692613132588053,0.4824148799525574,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,14.656014025909826,3.1264307498931885,1.7261542081832886,0.8465247675776482,0.046253156140252924,0.8638651221990585,0.11922210526933698,0.05914849787950516,0.05131917270605422,0.7704184979666024,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,4.8509798859013245,3.6838791370391846,3.184507369995117,0.8710947968065739,0.048361888272964976,0.9576142057776451,0.12680825755880265,0.06861184537410736,0.05544922426172013,0.7532464601099491,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,29.096665514167398,3.068984031677246,0.7015460729598999,0.8222361952066422,0.05595557607921993,0.776813130825758,0.24667247460135241,0.04654467850923538,0.04736410058470216,0.7437986480072141,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,4.865072590881027,3.452864646911621,2.9801976680755615,0.8543372377753258,0.052769682201152704,0.9538534767925739,0.1430208491165676,0.07054369151592255,0.062095815842545504,0.7648273929953575,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,29.086956684710458,3.3126308917999268,0.6725081205368042,0.8167429491877556,0.04259996975393548,0.7754366770386696,0.1571942906971861,0.05337901785969734,0.06011971347382121,0.7897549889748916,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,19.404886791831814,2.9144253730773926,1.305091381072998,0.8284611515700817,0.03886180986487038,0.7707101553678513,0.10729178921468686,0.045558929443359375,0.04836078751276183,0.7883601629873738,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,24.24424386373721,3.1053991317749023,1.1456058025360107,0.8155522793531418,0.039447295542892516,0.771204762160778,0.14043591102246622,0.049833666533231735,0.05194997041546484,0.770116247003898,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,14.611024820827879,3.0294432640075684,1.7596466541290283,0.8481163121759892,0.03809077205437368,0.8445475809276104,0.12322482173089383,0.05438043177127838,0.045012482936102605,0.7740382150514051,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",70,34.12425066984724,3.4553005695343018,0.6083991527557373,0.8253490775823593,0.04095396524317534,0.7858117781579494,0.17807147996768535,0.04424777999520302,0.03780300812512944,0.7786885290406644,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,29.10671283863485,3.0781924724578857,0.776206910610199,0.8287938311696053,0.05693019129977076,0.7814224250614643,0.21625174327498375,0.04885563999414444,0.04624589098180916,0.7656930120429024,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",70,33.96511132456362,3.333181381225586,0.7494524717330933,0.8251966498792171,0.05664577380293053,0.8017212674021721,0.2196892647632957,0.05086217448115349,0.05407860177465344,0.7793098550755531,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,19.415223131654784,2.972337007522583,1.4484821557998657,0.8216834589838982,0.061242249144495095,0.7949543632566929,0.13504961366665605,0.05389803275465965,0.048878433068018576,0.7611702469876036,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,4.867896174895577,3.78521728515625,3.169126033782959,0.8714874163269997,0.046381054423616536,0.9400273151695728,0.17038479672047308,0.0692010149359703,0.06533982364393838,0.7912035669432953,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,29.223502317327075,3.4365265369415283,0.8879654407501221,0.8310581222176552,0.05452553969823343,0.7647910993546247,0.14768958151153855,0.04905148223042488,0.053765252076962984,0.7600049929460511,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.008866089046932757,8.044174194335938,8.01951789855957,0.9743983596563339,0.007062646466897425,2.542580306529999,0.3387050539895889,0.36625306494534016,0.12147776663961367,0.7610943120671436,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,24.242047469946556,3.2069454193115234,0.998196005821228,0.8220493979752064,0.0502872354520162,0.7401996180415154,0.12614306765838193,0.038459762930870056,0.03176936579627212,0.7992066660663113,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,14.602003282052465,2.883497953414917,1.6799172163009644,0.8511332608759403,0.044319906207342954,0.8393236547708511,0.16232731399733508,0.054157041013240814,0.04744794349862967,0.7638672180473804,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,1.3663015069905668,5.989935398101807,6.007705211639404,0.9559942409396172,0.013569286391593813,1.6072055250406265,0.24847790363684277,0.16256906650960445,0.12299204882613551,0.07507233507931232,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,2.443647866952233,5.798981189727783,5.792640686035156,0.955321129411459,0.01766117742629224,1.5158503353595734,0.30615135237617436,0.15162111446261406,0.12659671501724143,0.07629444601479918,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",70,7.874692596960813,5.236358165740967,4.9566802978515625,0.9574887417256832,0.01605232316713341,1.3978207111358643,0.26041565630358904,0.14106330089271069,0.12319153425129525,0.07534250302705914,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,2.52152512606699,5.759494781494141,5.703444004058838,0.9577658139169216,0.014320799253108711,1.4833962731063366,0.2616060082173867,0.14759022742509842,0.11456680957926356,0.07639902096707374,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",70,7.765772928018123,5.498685359954834,5.160433769226074,0.9537991993129253,0.016154844755705295,1.392208743840456,0.2868901458647235,0.13996406830847263,0.12182809951817787,0.07556284801103175,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",80,8.947089520981535,5.22196102142334,4.851180076599121,0.9568182341754436,0.014311191862297418,1.3830357752740383,0.2959737965424178,0.13888064213097095,0.12670232158090958,0.07686138595454395,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,1.3191316470038146,6.161370277404785,6.173506259918213,0.9590462259948254,0.013539109703715283,1.646111011505127,0.23195784784580956,0.16904826276004314,0.12400422941789964,0.07512010203208774,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",60,6.584697713959031,5.3057379722595215,5.039726734161377,0.9591665342450142,0.016163992558419278,1.4153468683362007,0.2694007918748956,0.14283357188105583,0.1133079131070298,0.07497422699816525,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,1.3232343590352684,6.127321243286133,6.092583179473877,0.9573978744447231,0.014005628415499644,1.5847594514489174,0.23619120079969208,0.1609135866165161,0.11994883824565006,0.07661745697259903,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,3.5855137819889933,5.499384880065918,5.466382026672363,0.963165882974863,0.010059066161296698,1.4617141298949718,0.26013784213510416,0.1492599081248045,0.1208328145859541,0.07689210504759103,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",80,8.877967256004922,5.364228248596191,4.978330612182617,0.958314523100853,0.01568976895861948,1.3932699486613274,0.2624663391134936,0.1365352477878332,0.11419494851837135,0.07678763510193676,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,2.528604123974219,5.850131511688232,5.797353267669678,0.955535065382719,0.014500915804372048,1.5176428407430649,0.25499755793176976,0.1503083687275648,0.11297180640389254,0.07685376005247235,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",70,7.9200504559557885,5.492727279663086,5.212383270263672,0.9539210721850395,0.019129516896316884,1.4535628333687782,0.3368742362164119,0.14074396528303623,0.13311376675263828,0.07610426109749824,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",80,8.931104171089828,5.366450786590576,4.928360939025879,0.9567539766430855,0.014660114352189628,1.3977791853249073,0.28808867284578227,0.13934386149048805,0.12284846898376058,0.07580165599938482,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,3.5700708439107984,5.597221851348877,5.481849670410156,0.9592609070241451,0.016066502316377015,1.4430340863764286,0.24856157894576414,0.14823336526751518,0.11928558380399351,0.07673455600161105,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",100,11.03356784407515,5.346724033355713,4.7872090339660645,0.9537670649588108,0.01824242536596255,1.3960235603153706,0.31906632679947805,0.13963717967271805,0.12633452168877912,0.07506999000906944,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,4.534479380934499,5.491029739379883,5.335373401641846,0.9628315605223179,0.011007462642171024,1.4496034905314445,0.22340750922436042,0.1488183382898569,0.11754723834084979,0.07507171109318733,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,5.7787265710067,5.439942836761475,5.204453468322754,0.9578270874917507,0.01256840626432984,1.4201438054442406,0.28710571910846144,0.14547966048121452,0.12590844873517268,0.07584525307174772,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",100,11.24183316307608,5.157326698303223,4.826208114624023,0.9557202346622944,0.018984956071744958,1.3795915357768536,0.30009833537228403,0.1374167501926422,0.12481873592418408,0.07566030800808221,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,4.568338138982654,5.348299026489258,5.240436553955078,0.955323364585638,0.015373777019996298,1.4156952425837517,0.27716761803121853,0.13420295342803001,0.11391748205402084,0.0761339080054313,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",90,10.037020512972958,5.5249247550964355,5.126619338989258,0.9582534022629261,0.0081631741355026,1.4511751234531403,0.23671638408102072,0.1426866501569748,0.10504685547653149,0.07580861495807767,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",110,12.211774891009554,5.407287120819092,4.825474262237549,0.9545602761209011,0.024590571416068015,1.383839875459671,0.2825226967913166,0.1344885230064392,0.10532247139819055,0.07567513303365558,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,2.550127213005908,5.635326385498047,5.61452579498291,0.9617023169994354,0.01098317083279647,1.5027154460549355,0.24814343319708398,0.15625105239450932,0.12102730726603628,0.0761296289274469,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",70,7.996131987078115,5.389021873474121,5.101689338684082,0.9641432836651802,0.01021650897248191,1.4415959492325783,0.23478888872142906,0.13929353840649128,0.11052658282495244,0.07574283110443503,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.26099289301782846,7.194058895111084,7.229362487792969,0.9666458144783974,0.010481348808771053,2.17140956223011,0.26686631813521067,0.2529795877635479,0.14409359640518704,0.0759632569970563,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,5.786547687021084,5.485809326171875,5.267691135406494,0.9554143808782101,0.013775760657347457,1.4462097436189651,0.26638871360341393,0.1415700800716877,0.11669129315108995,0.07554681797046214,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",70,7.797649394022301,5.348803997039795,5.147640705108643,0.9592292308807373,0.007132814613145952,1.4150294214487076,0.26459705313095855,0.13261296041309834,0.1076746736986456,0.07615651399828494,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,5.801912594004534,4.798105239868164,4.65418004989624,0.9380185790359974,0.017572820597978763,1.1022317446768284,0.1584840821136138,0.06758303195238113,0.06261431176073076,0.28442546306177974,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",70,16.421566460980102,4.153716087341309,3.1406736373901367,0.921206459403038,0.026584828261551105,0.9167924374341965,0.2394901027894033,0.04211968556046486,0.03704898247106745,0.2580571719445288,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",80,18.5728238329757,4.1641106605529785,2.833343505859375,0.9085679724812508,0.03176573695637859,0.9081838391721249,0.24538448413506148,0.04264471307396889,0.048062481644695794,0.26151961798314005,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,9.93798406003043,3.9763643741607666,3.582035541534424,0.9111900702118874,0.02697583207425415,0.9635082297027111,0.16894768909844982,0.049806661903858185,0.03516732139024079,0.26015168603044003,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",90,20.641434854012914,4.536659240722656,2.5003089904785156,0.8898604549467564,0.030769922820171297,0.8798138424754143,0.2080256982671666,0.05059342086315155,0.04251770018679739,0.2575199679704383,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,11.926884461077861,3.791879892349243,3.301511526107788,0.9070712327957153,0.03314417902904593,0.9253710545599461,0.15924763326066246,0.048517756164073944,0.037315026124214036,0.25751809997018427,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",60,14.369611228001304,3.4916677474975586,2.7818005084991455,0.8929749131202698,0.03299322849076941,0.9246578402817249,0.16102323633381693,0.05962849780917168,0.049040343380431395,0.25493394094519317,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,3.29283201508224,5.4331746101379395,5.383187294006348,0.944764219224453,0.01332579238916443,1.2688825465738773,0.20330073444462388,0.0906108058989048,0.06888751576854824,0.2799742319621146,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",60,15.052378623979166,3.318864107131958,2.763554573059082,0.8850399516522884,0.03133003943330206,0.8989880047738552,0.18859151519187495,0.052331387996673584,0.04627217408782027,0.2811856819316745,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,2.7101279909256846,5.613521575927734,5.494119644165039,0.9448848329484463,0.01615029947492157,1.256344735622406,0.11753424535534109,0.09020056948065758,0.0675914009279522,0.2484754640609026,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",60,15.308640653034672,3.6359474658966064,2.8867733478546143,0.8920926339924335,0.03658006542762006,0.8780790716409683,0.16305741336580748,0.04708519205451012,0.03677929285947098,0.24519106303341687,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",80,20.311562245013192,3.82155442237854,2.5407254695892334,0.8837589435279369,0.030281356257390653,0.8577482663094997,0.12382766508300133,0.042949821799993515,0.034512068452781215,0.25431809190195054,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,7.470652007963508,4.207414627075195,3.9659500122070312,0.9252936244010925,0.018081805951290716,1.004415586590767,0.13383339981406933,0.0569169782102108,0.043052655826601026,0.2569560760166496,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",80,17.755906792008318,4.05717134475708,2.5654237270355225,0.8988055363297462,0.02682869318689865,0.9248494952917099,0.13780913080724763,0.060124512761831284,0.048991156295031695,0.24114144092891365,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.4842279669828713,8.028544425964355,8.047623634338379,0.9731752090156078,0.010689933881310593,2.6085314080119133,0.5562028157701842,0.3019838258624077,0.17464196144926958,0.24295920692384243,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,11.823537766933441,4.103989124298096,3.3484981060028076,0.9140332601964474,0.028932242143927842,0.9364750869572163,0.1754555856518002,0.04485217481851578,0.038324206314943446,0.24734510795678943,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",90,21.481564223999158,4.7646026611328125,2.6097192764282227,0.8991723544895649,0.03072515754907893,0.9165699407458305,0.16091526909745862,0.051472295075654984,0.04509857311173786,0.2483114480273798,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",60,13.847233393928036,3.5046606063842773,2.861079692840576,0.8777459263801575,0.03163768321604086,0.8803808242082596,0.18814641789403966,0.04758583754301071,0.03864619431882952,0.24352752801496536,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",70,16.97122731199488,4.528343677520752,2.831141233444214,0.8904418870806694,0.032928050816697675,0.9374268539249897,0.22719508200206162,0.0657014474272728,0.06492082940259777,0.26131720398552716,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,5.011676277965307,4.3920769691467285,4.26483678817749,0.9117434695363045,0.025949035466676654,1.023861676454544,0.16177388957649752,0.06966149434447289,0.05504191149722424,0.24288033798802644,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",70,16.3638174260268,3.447723627090454,2.752196788787842,0.8902286179363728,0.024879454643216118,0.906213779002428,0.18729519393778746,0.06341954693198204,0.05990956961997932,0.2528012269176543,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,2.7000162940239534,5.5573811531066895,5.484742641448975,0.9450058937072754,0.01759272652048885,1.2328393422067165,0.15436334223115353,0.08623820170760155,0.0729257197229237,0.2444314550375566,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",60,14.69535895017907,3.5665338039398193,2.8886561393737793,0.8990788795053959,0.036058825481121916,0.9068649634718895,0.15267919875864486,0.05286126211285591,0.0465701128008643,0.25352897006087005,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",80,18.1367186808493,4.09987735748291,3.046971321105957,0.9112545475363731,0.03238929375399376,0.9337055422365665,0.20311685065006052,0.04603469371795654,0.03441785733031045,0.24094283510930836,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,11.346651299041696,3.8998427391052246,3.5765538215637207,0.9049108475446701,0.033337121583664565,0.9607882834970951,0.16841887361570124,0.05528469756245613,0.05229933138407016,0.4392239070730284,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",80,28.97117006103508,5.131038665771484,2.4902801513671875,0.8929769173264503,0.039896297478751105,0.9045674242079258,0.21408223239353988,0.042979564517736435,0.037324046446978294,0.4401116870576516,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",90,32.73469433898572,5.19831657409668,2.0290181636810303,0.87039440497756,0.037059230771874875,0.8215423375368118,0.12283636488854441,0.045698754489421844,0.045114279866006815,0.44161361909937114,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,14.875728915911168,4.026320934295654,3.090686798095703,0.9068607985973358,0.02809585151280478,0.9808883890509605,0.17981113784393393,0.06311099603772163,0.0554551711418673,0.4250968359410763,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",100,35.44385247095488,4.977939605712891,1.2913964986801147,0.814601942896843,0.03343215483683342,0.7072892114520073,0.09216822375989182,0.037685129791498184,0.036602591519298684,0.4320612750016153,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,11.326444489997812,4.112370014190674,3.7615067958831787,0.9250779636204243,0.021340751906098077,1.0828709453344345,0.1574655391056131,0.06813368946313858,0.05279560822790939,0.4340841189259663,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,14.75294080004096,3.7004425525665283,3.1304054260253906,0.8991695307195187,0.021888485822465033,0.9642909802496433,0.13995397836385182,0.06815590336918831,0.06512355955922802,0.41912848700303584,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.7258976420853287,7.481116771697998,7.51593017578125,0.9700027517974377,0.010512798467521225,2.1986212134361267,0.38136698562383864,0.2624939251691103,0.15727294999061775,0.41925097396597266,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,18.18239360291045,3.533937454223633,2.703603982925415,0.8821413777768612,0.03257476834383507,0.897062212228775,0.14974476670741477,0.0547218956053257,0.05549760619138901,0.42868341400753707,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.7410820008954033,7.592860698699951,7.595389366149902,0.9705502763390541,0.010273019587810843,2.2682569697499275,0.4134032955043745,0.2697016950696707,0.16112857766809704,0.4252718250500038,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,8.39722358295694,4.193892955780029,4.074360370635986,0.9217225983738899,0.023974189177190824,1.2038862071931362,0.45781771077874756,0.07842112332582474,0.05218119992658379,0.43342038698028773,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",70,25.568985523073934,3.93626070022583,2.130296230316162,0.8585850410163403,0.045357719030569045,0.817758072167635,0.13919426742032148,0.053753435611724854,0.055576864143787326,0.4169175180140883,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,10.929896965972148,4.224432945251465,3.6101760864257812,0.9031960740685463,0.0280252829514069,0.9283486790955067,0.17614504060313252,0.04525775834918022,0.03867335764575467,0.40112656995188445,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",80,29.127107345033437,3.932894229888916,2.220855236053467,0.8685304671525955,0.032465328938971805,0.8324562832713127,0.14475688013460533,0.04059239849448204,0.03968380898385791,0.41305149300023913,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",90,34.61898786399979,4.148130416870117,1.6438558101654053,0.833407785743475,0.032096018154928706,0.7707181610167027,0.15449952118200921,0.041120342910289764,0.036948127497092254,0.43284081295132637,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,7.812702837982215,4.261989593505859,4.012927055358887,0.910097349435091,0.02677048605699641,1.0268983989953995,0.1353915665448259,0.06256211921572685,0.05001501043410914,0.41382317896932364,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.7511505050351843,7.554871082305908,7.51900577545166,0.9684452675282955,0.010538978003302715,2.203454852104187,0.37195210820933977,0.26380737125873566,0.15669234610851276,0.4191270830342546,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,17.513295267010108,4.335060119628906,2.640636444091797,0.8777148425579071,0.03807410901984079,0.882536519318819,0.16060161944556117,0.06006965413689613,0.05228718217414536,0.41324199095834047,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",60,23.334372148965485,3.595857858657837,2.3241097927093506,0.8673741668462753,0.0381179677955572,0.8325084708631039,0.14843465454592472,0.05003271996974945,0.04898603508826059,0.4155874520074576,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,4.143280155956745,5.362991809844971,5.297371864318848,0.9371658302843571,0.02017605915606379,1.119323007762432,0.14855500122845405,0.06097721680998802,0.04981875868287461,0.4104760909685865,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",60,21.09297748899553,3.752876043319702,2.474794387817383,0.8854092359542847,0.03779557166709609,88.87303642556071,340.596524098611,0.05630190670490265,0.045183069222801076,0.41246129001956433,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,7.729991549858823,4.285904884338379,3.969252824783325,0.9187034107744694,0.024123217943668774,1.1047074683010578,0.14202383194212453,0.0690065324306488,0.04659251802604715,0.4211577649693936,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,20.10433264495805,3.9383363723754883,2.7889609336853027,0.8810124360024929,0.03879934752810055,0.8990662582218647,0.1814252298969821,0.05909763649106026,0.05303353058694113,0.41049537900835276,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,4.603158529964276,4.136868000030518,3.9032421112060547,0.8958735577762127,0.05548298970309925,1.3365621753036976,0.3391813675845727,0.15192583203315735,0.11508134091174303,0.33512340299785137,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",70,16.171399667975493,3.686296224594116,3.109712839126587,0.88814852014184,0.057566147970750575,1.2796693481504917,0.3176220159841263,0.14731773547828197,0.11289390983945939,0.37578470597509295,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",120,25.54531372792553,3.297316312789917,2.415253162384033,0.8772311992943287,0.06440349618350712,1.1992263160645962,0.33533489101370423,0.13351280242204666,0.10914232086775076,0.36377906997222453,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",130,27.496946751954965,3.407292366027832,2.611084222793579,0.8855903744697571,0.056629777297443146,1.2346994206309319,0.36850298067457965,0.14069953188300133,0.12436081056993153,0.3610941891092807,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",170,35.67414004099555,3.2104005813598633,2.4837958812713623,0.881225548684597,0.06304298230459161,1.2286613583564758,0.33808306142660455,0.14119383692741394,0.11198272360018444,0.35786399501375854,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",60,14.366525569930673,3.5293281078338623,3.0914571285247803,0.8802849054336548,0.06920798035624404,1.2664452604949474,0.33666041831308635,0.14031179435551167,0.11438425305844345,0.39322784601245075,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",110,22.21727339993231,3.2927613258361816,2.475959300994873,0.8686854466795921,0.07516915670512615,1.2278196848928928,0.4000149425073277,0.13961873203516006,0.12178196363850095,0.35462588199879974,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",120,24.6260074370075,3.625309467315674,2.569704055786133,0.8797025717794895,0.07429268499999643,1.2655468434095383,0.3071587341947332,0.1386721506714821,0.10249247844650493,0.3549659620039165,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,6.33046936406754,3.952421188354492,3.646974563598633,0.8885217495262623,0.05818277611114906,1.3221542946994305,0.31400442082384855,0.14881301298737526,0.11136550203666062,0.3521350990049541,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",80,16.668944592936896,3.6657655239105225,2.8463475704193115,0.8916638642549515,0.05852629666333474,1.3148026540875435,0.3101046878451542,0.14939674362540245,0.10980861891043606,0.3559486489975825,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,6.428623012965545,3.9805898666381836,3.767953395843506,0.9138817936182022,0.05048312253933222,1.3572533167898655,0.35781784484974877,0.15861842781305313,0.11794298603996071,0.3592326130019501,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,9.320676233968697,3.668917179107666,3.343043565750122,0.8860204145312309,0.06300751563562605,1.257084459066391,0.3424657766983602,0.14519809558987617,0.11378229470767291,0.3793534570140764,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",90,17.68297835998237,3.2563600540161133,2.676654100418091,0.8755499012768269,0.06951125688068711,1.1952094435691833,0.33835799755829804,0.12972209975123405,0.10872203285414764,0.3587812000187114,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.44843950390350074,6.410867214202881,6.444538593292236,0.9589540213346481,0.013440973745556606,1.971814103424549,0.28660923899983887,0.2491417732089758,0.1469156049205289,0.3701235200278461,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,11.963374409009703,3.7743966579437256,3.332930088043213,0.9008508212864399,0.04610080822712344,1.3015304207801819,0.27936147444534093,0.14703469723463058,0.10283554879779563,0.3690042120870203,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",100,20.44646260002628,3.497737407684326,2.7414193153381348,0.8706670738756657,0.06613652637602584,1.2267471961677074,0.32521843327078437,0.13713869452476501,0.11518802104946582,0.36907715897541493,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.4449129189597443,6.422338008880615,6.4569244384765625,0.9546538963913918,0.01689032975265071,1.903723768889904,0.3182834883082569,0.23933268897235394,0.15140018625439722,0.364513176959008,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,11.258471623063087,3.5789928436279297,3.290130615234375,0.8959654755890369,0.06540353706806552,1.34079185500741,0.31666511176215695,0.1549960859119892,0.11118976199210528,0.3688078959239647,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",100,20.360362841980532,3.441638469696045,2.617356777191162,0.8775365315377712,0.05881659462768317,1.2209403477609158,0.3100512732108997,0.13519583083689213,0.10601522962973894,0.3698522759368643,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",110,22.35216729599051,3.6196539402008057,2.722041368484497,0.876649547368288,0.04715099387600509,1.254216279834509,0.32233272555222636,0.1395899597555399,0.10682144754750103,0.3673350039171055,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.4406816891860217,6.665661811828613,6.693512916564941,0.9615480192005634,0.01228604167720731,2.022685833275318,0.2766555966582419,0.25013011507689953,0.14618136344051436,0.35467298701405525,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,8.43252604897134,3.6240429878234863,3.3737335205078125,0.9002085700631142,0.06473303795850534,1.2953709438443184,0.3090821908129815,0.14941712096333504,0.11439049340652879,0.36438409611582756,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",110,22.22289787302725,3.403656005859375,2.5283093452453613,0.8732282780110836,0.06219019940472292,1.2388453930616379,0.3555362253998528,0.1390218511223793,0.1202219196927313,0.3690455190371722,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",120,24.39160053106025,3.428586483001709,2.501326560974121,0.8787857070565224,0.05351074056602407,1.2260746136307716,0.3284965041074458,0.1327014472335577,0.10871161142068046,0.36874770815484226,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,8.467077040812,3.691443920135498,3.405729293823242,0.9004489704966545,0.059609246879094094,1.2631333135068417,0.3601269828388596,0.13855825923383236,0.11589760633391229,0.36759730405174196,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",90,18.59232978709042,3.1356825828552246,2.6335296630859375,0.8729280158877373,0.0777785177171425,1.2235395424067974,0.33639825174947846,0.14175815135240555,0.11337238080618964,0.3735099611803889,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,2.4241852241102606,4.604547023773193,4.534778118133545,0.9165027029812336,0.04348202130287921,1.441680807620287,0.3323943314687162,0.16802389547228813,0.1218043620887443,0.3736713360995054,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,11.177763669984415,3.7286152839660645,3.344694137573242,0.8933414332568645,0.06757514477936535,1.3074114173650742,0.3202465937355005,0.14560703188180923,0.10791905300925501,0.3861388280056417,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",80,16.41675656987354,3.550567150115967,2.9432241916656494,0.8735391199588776,0.07191998948633391,1.20851493999362,0.3267865041201829,0.13589587807655334,0.11222647600332036,0.3736306349746883,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",130,26.671598098939285,3.1051721572875977,2.4951510429382324,0.8949849419295788,0.053330408662492244,1.2731810212135315,0.3471500866674272,0.14831290394067764,0.1122214571058766,0.3723672330379486,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",180,36.452779734972864,3.270738124847412,2.1593291759490967,0.8816247284412384,0.054757480606115655,1.2665879726409912,0.3197807407547462,0.14280106872320175,0.1114961524612815,0.37275623995810747,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",190,39.75170021294616,3.313321590423584,2.1271495819091797,0.8700280971825123,0.06592554482484979,1.2159578241407871,0.3078555268244263,0.13762680813670158,0.11211291881484414,0.3719573151320219,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,8.319968261988834,3.866360664367676,3.4920997619628906,0.8968181535601616,0.04782991318684441,1.3122158087790012,0.31156343356795174,0.14832409285008907,0.11142681983722766,0.3707547499798238,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",80,17.34240167797543,3.2833127975463867,2.7916054725646973,0.8793652728199959,0.06700544184204099,1.2065871804952621,0.35053713399669084,0.13788849487900734,0.119847302875624,0.37638736702501774,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,5.691105982055888,3.5669357776641846,3.3624181747436523,0.8808293528854847,0.045201442910083385,0.9425242878496647,0.10206845234023997,0.06564345955848694,0.05814119009097943,0.9528390959603712,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,10.651024811086245,3.410832166671753,2.906259059906006,0.8663618080317974,0.038420439241507454,0.9377255439758301,0.11940609919879856,0.06838129833340645,0.052821566716453404,0.9611801089486107,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",70,33.85510757192969,3.0357937812805176,1.581432819366455,0.8487514182925224,0.048936189611972214,0.7904449366033077,0.1678088483049924,0.045735809952020645,0.04129629574787916,0.9628430539742112,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,17.235177733004093,3.20220947265625,2.347456455230713,0.8487551435828209,0.06052724346683608,0.8281082324683666,0.14112804664902687,0.056604038923978806,0.05828283710082462,0.9338504729093984,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",80,43.92987788107712,3.151658296585083,1.191517949104309,0.7993131726980209,0.050183785372489055,0.6811894532293081,0.13326708004467838,0.040275320410728455,0.04861799234359984,1.0630033010384068,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",100,52.376393234939314,3.241826295852661,1.0447676181793213,0.8132945485413074,0.05506885443271709,0.7145709339529276,0.1543510994899532,0.039269525557756424,0.03435075447388846,1.0728621769230813,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,20.898540282039903,3.1232306957244873,1.9910988807678223,0.8297720476984978,0.05381720568333398,0.7546245232224464,0.14255871431032308,0.0455181859433651,0.04429202968926956,1.0524464149493724,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",90,44.92091269104276,3.6678900718688965,1.1933400630950928,0.8303800150752068,0.05948539690205832,0.7902462780475616,0.21121458573555632,0.05160636454820633,0.05439106460961038,1.051136659923941,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,10.81947101908736,3.3741862773895264,2.861476421356201,0.8544281907379627,0.05139812526745879,0.8964799828827381,0.13834121593425078,0.06493626534938812,0.06045950816660897,1.053686264087446,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,15.485667948960327,2.921786308288574,2.193664312362671,0.8508891388773918,0.04304675419222804,0.8056657426059246,0.10409750391801041,0.045706361532211304,0.041176342042233964,1.0530450170626864,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",70,36.17816080804914,3.0705180168151855,1.3553860187530518,0.8106055781245232,0.03966953134849518,0.7307357266545296,0.12333393166454155,0.044073522090911865,0.04151360018830702,1.0493802060373127,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",60,30.60975751199294,2.809605121612549,1.4991590976715088,0.824795451015234,0.06694614188317294,0.7466777078807354,0.18395140020488324,0.04590773954987526,0.0413008420216644,1.0661845040740445,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,5.793439056025818,3.8082683086395264,3.5380630493164062,0.8762821294367313,0.0417979371860702,1.0070652104914188,0.10770015187338619,0.07693149521946907,0.057836790711624106,1.0578602380119264,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,10.590828103013337,3.211867332458496,2.8238472938537598,0.855867300182581,0.04484750752198163,0.9164181351661682,0.140063411917865,0.06510624662041664,0.053991912902458965,1.0544455929193646,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",70,38.12397689302452,3.0046186447143555,1.2550005912780762,0.8161340206861496,0.05490587064240977,0.7403460927307606,0.11907288717597615,0.04072690010070801,0.04073985397221539,1.0786332449642941,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,10.71786366798915,3.0979461669921875,2.588613748550415,0.834104523062706,0.06623322118714728,0.8316177576780319,0.09594677552768589,0.052352871745824814,0.05104527588230157,1.083105952013284,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",60,30.321652960032225,2.884204626083374,1.3842198848724365,0.8237009532749653,0.0426812243848608,0.7260870039463043,0.14891936607248085,0.03883657604455948,0.03368787975476728,1.098260328057222,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",90,45.88139608199708,3.1212317943573,1.03043532371521,0.8098459951579571,0.04117683228199809,0.7213863991200924,0.14723995494887482,0.03693702444434166,0.02636009111412523,1.084123036940582,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,10.86658365803305,3.3631792068481445,2.7470366954803467,0.8598331771790981,0.04565555114755294,0.9243496954441071,0.1269974597127992,0.06769508123397827,0.057908660581814805,1.0772612520959228,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",70,36.41086269298103,3.1038308143615723,1.413622498512268,0.8040474951267242,0.056241072462811494,0.6987394168972969,0.1310382889075064,0.03305686637759209,0.028315001453026165,1.0771400600206107,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,20.34281454794109,3.0715436935424805,1.9032914638519287,0.8273321203887463,0.05754789122485512,0.7807545624673367,0.12179796864503016,0.046975795179605484,0.045070261637928696,1.075259329052642,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,25.11860703700222,2.7935311794281006,1.6524407863616943,0.8183837682008743,0.05070116209523586,0.753894180059433,0.0983613423648343,0.04273242503404617,0.03635277874858751,1.0690222571138293,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,1.0347592842299491,5.9986090660095215,5.925783634185791,0.9468076378107071,0.0167029393426863,1.6269589886069298,0.21228971570918512,0.17428474128246307,0.1127966578180523,1.0619706430006772,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",70,37.86897384491749,3.0396995544433594,1.4992091655731201,0.8292820826172829,0.061157350093855464,0.7737432960420847,0.14561774415896872,0.04594096168875694,0.038372137840650786,1.0669212909415364,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,28.060979058034718,3.354431629180908,2.2281136512756348,0.8587692975997925,0.04345686160285865,0.8487597294151783,0.12396039621554021,0.05077367275953293,0.043973059050390825,1.837515837047249,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,36.893538621021435,2.9792234897613525,1.5871309041976929,0.8146975822746754,0.05248085183531811,0.7462290823459625,0.1407352957571018,0.048731815069913864,0.06914153007895693,1.9581664920551702,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,10.197592265089042,4.051432132720947,3.624361753463745,0.8819853663444519,0.04406337511970027,1.0282788462936878,0.17374887537385414,0.08203902840614319,0.06323059119342518,1.720759314019233,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",60,48.925923660979606,3.4852135181427,1.4708093404769897,0.8502816930413246,0.033919537948321817,0.7831603176891804,0.12164616265259574,0.04560711607336998,0.05121040656098554,1.6397141820052639,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",100,84.90830351202749,3.593844175338745,0.4710679054260254,0.8121662735939026,0.05323860477385917,0.7007520347833633,0.12163303060691535,0.03137088567018509,0.021233136906594792,1.864217960042879,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,17.26133198395837,3.1052942276000977,2.5179624557495117,0.8452443517744541,0.05726162216055648,0.8230507783591747,0.08781904084314658,0.051965996623039246,0.04969978179971316,1.6449563431087881,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,1.8264782769838348,5.91974401473999,5.837311744689941,0.9446415193378925,0.01685066831462771,1.5869047418236732,0.17404836187685915,0.1663811169564724,0.10883100725460369,1.7006442489800975,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,40.11847254598979,3.0999457836151123,1.4238497018814087,0.8144524805247784,0.05379262514441251,0.7217365801334381,0.1309579867862147,0.04481963813304901,0.05325100090282961,1.6462487910175696,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,1.956114195054397,5.822977542877197,5.787508487701416,0.9393669068813324,0.020090642722045526,1.4876565262675285,0.19068433492671333,0.1484670527279377,0.10946042196276705,1.8905839389190078,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,41.348319452023134,3.257047176361084,1.6690038442611694,0.8238503336906433,0.046262811220240486,0.7634745202958584,0.14230259782206248,0.05055888369679451,0.05241137445686134,1.6243560269940645,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",70,56.37417688802816,2.9570703506469727,0.9251176118850708,0.8148234859108925,0.048424845257784004,0.7109441570937634,0.10874375348380658,0.03952240198850632,0.041938290740317415,1.6427857230883092,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,9.078562124981545,3.719576835632324,3.298393726348877,0.8789660967886448,0.04155752150021757,0.9792350418865681,0.11094321135096748,0.07220672816038132,0.05873424979952405,1.6119319020071998,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",60,47.26278779900167,3.2728402614593506,1.1981847286224365,0.8434085287153721,0.04873901909230848,0.7852781563997269,0.12620599344002775,0.04740126058459282,0.04227051520740163,1.638739768997766,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,1.5895564620150253,5.923954486846924,5.87482213973999,0.9472674280405045,0.016535632175401847,1.5962663739919662,0.2000919069131702,0.1698168832808733,0.11946658556258709,1.6341641150647774,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,42.859902839991264,3.100146770477295,1.3016722202301025,0.8205276727676392,0.0396989474064653,0.7452364414930344,0.1144938000338084,0.042697880417108536,0.03737873608068602,1.6305062930332497,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,16.99728308408521,2.9403927326202393,2.352740526199341,0.8564443290233612,0.044398313893873845,0.850374385714531,0.11515749176978522,0.059488698840141296,0.05549680106292641,1.6343982820399106,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,24.642722215969115,3.3967549800872803,2.223736047744751,0.8433202616870403,0.050625150581854766,0.8332765996456146,0.10278746834712639,0.05601516738533974,0.05450029704785285,1.6242755060084164,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,32.33255415898748,3.134507179260254,1.781339406967163,0.8470166735351086,0.03754101628164452,0.7803194113075733,0.12383080896880275,0.04782736673951149,0.050482838840582286,1.6353261249605566,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,9.168415518011898,3.7623372077941895,3.420328378677368,0.8851288072764874,0.03365211146185542,1.011032372713089,0.10191291511477654,0.07018711417913437,0.05383525680935816,1.7751168119721115,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",60,47.40321375313215,3.07464599609375,1.078263521194458,0.8252573981881142,0.04794992853445868,0.7462147064507008,0.1342407942714694,0.050165314227342606,0.054922402627100624,1.625529520213604,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.5439714931417257,5.161465644836426,5.190922737121582,0.9406170658767223,0.01811620544819752,1.3361459150910378,0.13525122765461195,0.11963802389800549,0.08576422854518985,18.154547353973612,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,2.681236929143779,3.8134193420410156,3.6687159538269043,0.9255722612142563,0.017827446040336203,1.1325816325843334,0.22148872613328172,0.08725948631763458,0.07436167559620734,17.605827767052688,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",80,4.3511484522605315,3.6875782012939453,3.3894715309143066,0.9181834831833839,0.0193927358492824,1.1083227023482323,0.18680622612605813,0.0836164578795433,0.056705165737605964,19.287067707977258,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",130,7.425519098993391,3.265784740447998,2.7179014682769775,0.9120840542018414,0.023995847480188515,1.0983343720436096,0.2275703560247745,0.08614060655236244,0.06264929458499557,19.0615336910123,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.0019165340345352888,6.300264835357666,6.2991461753845215,0.9582833647727966,0.01304942327175433,1.9369606897234917,0.2661504828424079,0.2506663277745247,0.14229006633265154,17.9450802130159,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.525083941873163,5.124337196350098,5.07110595703125,0.9372876286506653,0.01884914628206233,1.3352188766002655,0.12159946076959535,0.12009085901081562,0.08536569264040132,17.569933147053234,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,3.1765201261732727,3.941711187362671,3.5815367698669434,0.9090561419725418,0.02883768173714317,1.0477322041988373,0.23154425330150422,0.06886786967515945,0.04377144555847824,17.50091867300216,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",100,5.332509063184261,3.652615547180176,3.176661968231201,0.9040222391486168,0.026140019487954694,1.0244212411344051,0.21324114411594777,0.06376763433218002,0.03974296623545581,18.798823417979293,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",170,8.970604700152762,3.335676670074463,2.64176082611084,0.8849789761006832,0.024115682483851122,0.9951976500451565,0.21002163224679077,0.06795414909720421,0.03794873901045776,17.354421705007553,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.0020360880298539996,6.165063858032227,6.164417266845703,0.9581326395273209,0.015672598443759428,1.927966296672821,0.2606238855237538,0.24897744692862034,0.14115302875686173,17.452978065004572,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,2.7040188644314185,3.815053939819336,3.6803417205810547,0.9138814248144627,0.026219119572513945,1.1674847193062305,0.2809529095656932,0.0933685451745987,0.07184440011251012,17.56195890705567,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",100,5.310196104226634,3.3015496730804443,3.0031816959381104,0.8951113000512123,0.03799788469760879,1.1257867254316807,0.321614489511939,0.09236471354961395,0.06943618210849731,17.295591750065796,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,1.5686516355490312,4.176458835601807,4.1254167556762695,0.924771822988987,0.021869430844153215,1.2433697544038296,0.17921099018678663,0.10660622268915176,0.07066895109930667,16.673698458005674,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",70,3.6258217942668125,3.5947108268737793,3.533799171447754,0.8894371427595615,0.03896440892321715,1.116578385233879,0.205816804768013,0.09579126164317131,0.0675993148237295,17.07338907092344,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",100,5.212350073154084,3.0106186866760254,2.7891836166381836,0.8787220492959023,0.03912373402398812,1.0701125636696815,0.19764029917084908,0.0850205272436142,0.051474253286129486,16.915513633051887,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",150,7.839822221780196,1.6360310316085815,1.3157753944396973,0.8772899396717548,0.038500245511273824,1.0704689249396324,0.19369685995314856,0.08936958014965057,0.05820601733664174,17.05202230089344,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.5267660738900304,5.052338600158691,5.067374229431152,0.939544178545475,0.016964889028907477,1.3654832392930984,0.12274639271974173,0.12507652677595615,0.08525560219174909,16.960893795941956,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,1.0536881397711113,4.3967390060424805,4.396317005157471,0.9315166473388672,0.020309690189176194,1.2633667178452015,0.14199674480189145,0.10909216478466988,0.06558428993240853,16.96195541403722,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,3.2853687746683136,3.4650990962982178,3.188149929046631,0.9094839431345463,0.02770014193380568,1.1595855988562107,0.30601839701582756,0.10293165594339371,0.08666876452453483,16.951498907990754,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",130,7.0225102009717375,3.7864937782287598,3.1496949195861816,0.8895567692816257,0.029694305888756593,1.060620341449976,0.30819663978675227,0.09774763509631157,0.09939202594012556,16.963619654998183,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,2.105010879924521,3.818880796432495,3.554340362548828,0.928438413888216,0.019700932346018673,1.1524743363261223,0.2003672578033901,0.09305340237915516,0.08631471212987883,17.175282869953662,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,2.631687343819067,4.0412750244140625,3.676391839981079,0.9273058213293552,0.02053238951968286,1.1059735119342804,0.13093014645864748,0.0855492353439331,0.05708928208495998,17.36722869402729,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.0017865789122879505,6.223560333251953,6.2215399742126465,0.9566059336066246,0.014325781386153633,1.9328295215964317,0.2702150353232847,0.25018021650612354,0.1417706176341398,17.024416950996965,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,2.6291049115825444,3.4710471630096436,3.2821600437164307,0.9090629257261753,0.027372144237580696,1.0660708881914616,0.1455428157388514,0.07638055831193924,0.056891283204961694,17.003112302161753,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",90,4.742706349818036,3.2246320247650146,2.9965403079986572,0.8943511843681335,0.028669214270883363,1.1116970516741276,0.2525538574634201,0.08740431815385818,0.05417472789712661,16.911214939085767,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",120,6.330028949305415,2.3585803508758545,2.1074204444885254,0.8855618312954903,0.03219552288077974,1.0350972898304462,0.218829783466099,0.07145717367529869,0.0336646430303127,17.121846183901653,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.5296720878686756,5.349249362945557,5.32623291015625,0.9413776136934757,0.018633161873211835,1.3744341433048248,0.1485302391845123,0.1327648777514696,0.09784928009597264,17.20649583078921,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,3.232513379305601,3.6715664863586426,3.3538529872894287,0.9126882143318653,0.025507030233515332,1.0752383135259151,0.2834300406153537,0.07801471650600433,0.05721173788057987,16.9296583738178,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",110,5.875565299997106,2.9629733562469482,2.581690549850464,0.8979526199400425,0.023659069275657005,1.0134031772613525,0.29432044156215403,0.06480464711785316,0.04949164865429733,16.871052764123306,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",120,6.403246850240976,3.192800283432007,2.719728469848633,0.9020086191594601,0.017210537274484992,1.069794688373804,0.3211565675411314,0.07468706741929054,0.047806827380360024,17.06972796889022,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.5305326266679913,5.313189506530762,5.308628082275391,0.9390284940600395,0.017758514303967406,1.3729035779833794,0.13150851352829607,0.12826785445213318,0.09141689734788093,17.115994727006182,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,2.1094301745761186,4.291114807128906,4.1258063316345215,0.9207141175866127,0.025886878292346115,1.1874863617122173,0.18596511387857897,0.10975107178092003,0.07491267829446886,17.02432706602849,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",90,4.747969398507848,3.0551815032958984,2.60819673538208,0.8905664533376694,0.03229561453903143,1.1113865822553635,0.2536086133949179,0.10166389122605324,0.09357477729948621,16.92934673302807,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",100,5.271872597746551,4.1336283683776855,3.6778929233551025,0.8862282335758209,0.03407579215320917,1.0340379476547241,0.23720012563913734,0.07811744883656502,0.05550287219674462,17.053237890126184,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.0019128569401800632,6.287819862365723,6.289421081542969,0.9569096229970455,0.014972835228483864,1.9497529119253159,0.2718726457712659,0.25512088276445866,0.14363031629546877,17.061262637842447,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,2.828192538814619,3.8378961086273193,3.6973655223846436,0.9034422188997269,0.029982864188402163,1.0659935548901558,0.17733045297015296,0.07308072224259377,0.04545489305495647,17.318430229090154,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",90,5.112970136804506,3.317178249359131,3.0547890663146973,0.8795206882059574,0.03737990270227945,0.956393837928772,0.18057450778361733,0.056775711476802826,0.03626877775294012,16.927006841171533,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",120,6.694616065127775,2.9092330932617188,2.5974488258361816,0.8781143091619015,0.034851652579917104,0.9540009908378124,0.2296453396514788,0.0656760148704052,0.06532943590109277,16.86591693898663,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,2.4261732153827325,3.830886125564575,3.658856153488159,0.9129938445985317,0.02870516979679094,1.1488600000739098,0.17073580438892064,0.09150209277868271,0.06944154056957984,27.218210884951986,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",80,6.206924612517469,4.103132724761963,3.17527437210083,0.8972783870995045,0.027662342846079612,1.02121477201581,0.18723304486244188,0.07742845639586449,0.07161846872313188,21.396472907043062,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,1.5277631360804662,4.462474346160889,4.313760280609131,0.9292896129190922,0.018758974145834037,1.2096953578293324,0.16350317394979033,0.10055778548121452,0.06947969942832101,21.651128905941732,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,2.279280470102094,3.79728627204895,3.54374623298645,0.9251434244215488,0.019400425498767974,1.2223232686519623,0.1379943469970721,0.09968642145395279,0.06269622315872657,22.03218411304988,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.7601486929925159,4.913077354431152,4.869276523590088,0.9346948266029358,0.0173645220744992,1.3146109879016876,0.20693469168214748,0.11780174821615219,0.09110672995010884,23.271789966034703,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,3.0288638117490336,4.317424774169922,3.7177670001983643,0.9167794771492481,0.016658302011366352,1.216085258871317,0.17821471301894565,0.09837044402956963,0.04636668626335344,21.739800671930425,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.7428674328839406,5.232326984405518,5.138051986694336,0.936705831438303,0.018417378272201274,1.2910899817943573,0.15214586059030608,0.10804649814963341,0.07095893586597786,22.783021818031557,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,1.48483360290993,4.386317253112793,4.130674362182617,0.9291998632252216,0.018645017378944247,1.230190172791481,0.11861605325903779,0.09822362661361694,0.06916191721416126,22.092683948925696,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",70,5.535971135832369,4.217832088470459,3.337723731994629,0.9104035720229149,0.024487603204722694,1.1485939510166645,0.3056629498056079,0.0954197496175766,0.07502463081223729,21.68920611892827,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,2.2572748460806906,3.490633249282837,3.3003392219543457,0.9264857806265354,0.020323069092845453,1.2190227136015892,0.24854712430715037,0.10626120865345001,0.07745631943166296,21.965321533964016,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,2.3536712074419484,4.148287296295166,3.6582698822021484,0.921479593962431,0.021939936937251454,1.1898574233055115,0.15649480457001358,0.0991106815636158,0.06811098979206401,22.7799805739196,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,3.1046775116119534,4.3182830810546875,3.7855968475341797,0.9108901806175709,0.024884390061216587,1.1319324672222137,0.17258621110360747,0.09271641448140144,0.07088495361923297,21.970799911068752,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",90,6.793611021596007,4.014057159423828,3.0261383056640625,0.8941362053155899,0.030389851604297437,1.0590846426784992,0.231781081835623,0.0854598842561245,0.07738386052856656,20.700670872000046,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,2.4033034406602383,4.379939556121826,3.983283042907715,0.918243482708931,0.019686541518014414,1.1053296029567719,0.138057824457368,0.07980044186115265,0.04884689240962113,20.65901958697941,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,1.5345912970369682,4.163113594055176,4.044166088104248,0.9307237975299358,0.020831620704864967,1.2425899654626846,0.20318661655730136,0.10704335942864418,0.07233266543851849,20.61880448902957,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,4.550234362366609,3.8338794708251953,3.1426844596862793,0.9062222018837929,0.030109387874736354,1.1320315636694431,0.24818308033485006,0.09108594059944153,0.07188367216283911,20.563468171982095,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",90,6.81987588328775,3.9898836612701416,3.0123822689056396,0.9010004438459873,0.023765339128930175,1.2203862480819225,0.30138076764703886,0.11409762501716614,0.08508101217106892,20.64046749297995,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,3.0682422070531175,3.850677490234375,3.3056797981262207,0.9063397534191608,0.022943698352546144,1.057925634086132,0.14944267845430237,0.07572665065526962,0.05890740079370684,20.087234653998166,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.0019329750211909413,6.091094017028809,6.091227054595947,0.9588308036327362,0.014390507391208354,1.9364586472511292,0.28048427430095285,0.2527779769152403,0.14518691103111478,20.48899143608287,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.7502585359616205,4.749612331390381,4.611159801483154,0.9360046796500683,0.0178840294910769,1.348748654127121,0.1743506941352281,0.12554756179451942,0.08214480558323856,21.24278084607795,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,4.504616727004759,4.200234413146973,3.4020025730133057,0.907226201146841,0.02375027898534355,1.156435139477253,0.2617226943633809,0.08968648687005043,0.04823753511639796,21.043679184047505,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",90,6.731570807402022,4.738080024719238,3.4646337032318115,0.8991414196789265,0.020075422639401114,1.1242761127650738,0.2732443000581445,0.09082657471299171,0.06315920204721111,20.239012877922505,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",60,12.650038475287147,3.4285812377929688,3.449709892272949,0.905089970678091,0.06065976273791656,0.9270630963146687,0.2177233549424036,0.03714679926633835,0.027584770715102028,2.6249160210136324,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",70,14.631856663152575,3.2880895137786865,3.2278387546539307,0.8999956846237183,0.06956667873364034,0.9082526229321957,0.22481773017662846,0.040058478713035583,0.03197014922704649,2.701595257036388,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",120,24.81882167677395,2.3529508113861084,2.4136531352996826,0.85012486577034,0.06777935497167564,0.7925723120570183,0.25331799785192327,0.03280129283666611,0.03220789280304727,3.2308534630574286,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",170,34.8844341711374,2.135606050491333,2.1198182106018066,0.8395088352262974,0.07852331323457228,0.7329221703112125,0.25763209034186196,0.028673812747001648,0.03309576333801914,3.284375418908894,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",220,44.914834147901274,2.0273025035858154,1.9704409837722778,0.8321205824613571,0.0773046605047043,0.7106872759759426,0.24987741754201565,0.02432934194803238,0.028848812407714385,3.4016873649088666,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",10,1.9697013340191916,5.840418815612793,5.952892780303955,0.9673160277307034,0.01792814550485379,1.504698596894741,0.2906064464081927,0.09246236830949783,0.0654020658941441,2.1541200280189514,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",40,7.931389855104499,4.17327356338501,4.275392532348633,0.9347809888422489,0.03723874678491838,1.0826424770057201,0.2009521711891906,0.05268506705760956,0.036944014807585156,2.247716079931706,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",90,17.8787173058372,2.976919174194336,3.046172857284546,0.8873887918889523,0.06069464048620892,0.8821595683693886,0.21711964264937403,0.04234126955270767,0.038420381150778,2.7559009139658883,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",140,27.720896272337995,2.401707410812378,2.4723153114318848,0.8699964135885239,0.05311374337181361,0.8099829740822315,0.15444673875786916,0.03414329141378403,0.02210696347099893,3.2617558430647478,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",150,29.67511642514728,2.410123109817505,2.384305715560913,0.8682554140686989,0.05577555092972771,0.7881837636232376,0.16164316594946376,0.03162726014852524,0.02504283377464148,3.347691418020986,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",200,39.624762969557196,2.210433006286621,2.210286855697632,0.8464027382433414,0.07274573087365821,0.7841315418481827,0.1651403352388865,0.033467330038547516,0.029687417009683147,3.4251328699756414,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",230,45.57613655086607,2.0902438163757324,2.0659782886505127,0.8332209028303623,0.06682497877901851,0.7441911697387695,0.15800551559804668,0.027523040771484375,0.018359407388660667,3.458634066977538,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",60,12.005055986577645,3.615365982055664,3.6064109802246094,0.9097015336155891,0.0577661016498011,0.9375706054270267,0.20541569708907215,0.040929730981588364,0.02745440353136065,2.7913610349642113,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",70,13.949075301643461,3.325211763381958,3.3077893257141113,0.8986810483038425,0.06609445846103924,0.9356500990688801,0.20178284182156697,0.043826423585414886,0.0303901725078582,2.8546289859805256,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",120,24.155821453081444,2.570275068283081,2.504347562789917,0.8588275276124477,0.062427440493423254,0.763375535607338,0.21257010722221845,0.026160158216953278,0.02264735458053535,3.405098187038675,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",170,34.16030352457892,2.2181344032287598,2.2352852821350098,0.8317593485116959,0.07689745311257624,0.7868134528398514,0.18905979570311998,0.03253626823425293,0.024835656000808226,3.6284124410012737,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",210,42.16126995556988,2.0843355655670166,2.0929346084594727,0.8286465369164944,0.08148231401751428,0.7236568070948124,0.2098194063233988,0.028900787234306335,0.024793906672331903,3.787555052083917,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",20,4.268093540100381,5.273571491241455,5.325008869171143,0.9602655209600925,0.022629292600079145,1.3442417755723,0.24157003657205958,0.06869393214583397,0.04820907626226081,2.318937922012992,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",70,14.274897686089389,3.1986725330352783,3.2505733966827393,0.9013971425592899,0.057950916584683085,0.960861150175333,0.19268140783928014,0.0475337840616703,0.03132123820525039,2.886610638932325,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",120,24.584274082444608,2.516491651535034,2.5179989337921143,0.858398612588644,0.07871607924236615,0.7725737392902374,0.2058246616992928,0.027567237615585327,0.02360634865196396,3.4757440630346537,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",170,34.57580647536088,2.2150723934173584,2.2148795127868652,0.8327069990336895,0.08093108107343165,0.7227630726993084,0.17309368725342678,0.02528437227010727,0.018068756721342574,3.656004949938506,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",180,36.55004529596772,2.3028526306152344,2.164762258529663,0.844178918749094,0.07093480448604937,0.7353010643273592,0.23611556911877601,0.025038965046405792,0.020599350401266966,3.6465618340298533,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",260,52.75543973047752,1.9103760719299316,1.8650563955307007,0.812620684504509,0.06893695045243026,0.7041455153375864,0.22919573269379076,0.023939624428749084,0.022031828724284535,3.6812240229919553,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",290,58.74824863311369,1.900423288345337,1.8165446519851685,0.8273881822824478,0.0734080704588289,0.7376917004585266,0.2374933221976121,0.029556818306446075,0.026859696587497983,3.691049471963197,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",20,4.204881281126291,5.299160003662109,5.364143371582031,0.958312138915062,0.02199791034708485,1.326796505600214,0.2662235785762302,0.07011992111802101,0.047243380702645055,2.215190697927028,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",30,6.176286555826664,4.689217567443848,4.705828666687012,0.949034757912159,0.030283552540075667,1.221973042935133,0.21075756100869372,0.06316181644797325,0.03733705961086252,2.2785490010865033,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",80,16.140061591286212,2.9279022216796875,2.9567835330963135,0.8803120329976082,0.05809503870559551,0.8708816766738892,0.19461407386905388,0.041513845324516296,0.036204345537481114,2.89838550100103,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",130,26.325669686775655,2.3996427059173584,2.3983280658721924,0.8517149090766907,0.0730042182435284,0.8011973686516285,0.27212772041498506,0.03543608635663986,0.03465181993955703,3.1923182259779423,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",170,34.77905863733031,2.2984659671783447,2.1713290214538574,0.8311749324202538,0.060675442240554364,0.7736273035407066,0.20102536666729678,0.03252057731151581,0.027876988670231258,3.4192305631004274,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",20,4.339182932162657,5.295803546905518,5.371463298797607,0.9598984718322754,0.021123104575233215,1.363171249628067,0.254077428300455,0.07210886478424072,0.056512611640658725,2.1968591581098735,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",70,14.415884788846597,3.279712677001953,3.2978909015655518,0.8966073505580425,0.06708922140825982,0.9456053525209427,0.23498353758550736,0.046652596443891525,0.03950787329016144,2.887154384981841,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",120,24.450656870612875,2.5150532722473145,2.4856228828430176,0.8611108288168907,0.07693082771617525,0.8365660011768341,0.22895763848635425,0.03804813325405121,0.03390472066629389,3.2857557609677315,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",170,34.47593390499242,2.1653199195861816,2.163532257080078,0.8463457077741623,0.08325385140567247,0.7525192610919476,0.26613585316844385,0.03267267346382141,0.034276156969188225,3.318580335006118,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",180,36.47425134992227,2.051323890686035,2.106567859649658,0.8480834402143955,0.08884708808025965,0.7811877764761448,0.2622917086385939,0.03248142451047897,0.03362383872142367,3.419111429946497,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",260,52.7724135927856,1.9330499172210693,1.8678617477416992,0.8117980286478996,0.07826932055696,0.7014210373163223,0.31616194357394123,0.02866947650909424,0.035675730058913925,3.5335346199572086,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",290,58.78486991091631,1.794740080833435,1.7460304498672485,0.8279145248234272,0.07645417096675461,0.691865187138319,0.27939636617712965,0.026299111545085907,0.031088352676179046,3.691438007168472,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",340,68.75149568868801,1.7939906120300293,1.7412554025650024,0.833252802491188,0.09854751023549005,0.6881725359708071,0.28414310628904904,0.026624515652656555,0.031040796612356283,3.8689068011008203,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",350,70.74274060013704,1.7064129114151,1.6665652990341187,0.814792700111866,0.07846521903319265,0.670428516343236,0.2630803276636273,0.023383505642414093,0.02549829367255297,3.7247023398522288,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",0,0.0019798020366579294,7.133497714996338,7.1281890869140625,0.9777222126722336,0.012471291690100977,1.9349364638328552,0.31800054333077815,0.1651629488915205,0.10727463432148804,1.0776148338336498,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",50,10.019797974498942,3.9503421783447266,3.9982168674468994,0.9253850840032101,0.04527679308234674,1.0312457010149956,0.2027026571696958,0.05237199738621712,0.03886850276907838,2.558705599978566,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",80,16.01029422134161,3.036862373352051,3.1479296684265137,0.8978896327316761,0.05326861176293322,0.9019330255687237,0.21560407440487683,0.040636129677295685,0.02495084902760457,2.910727591952309,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",120,24.021352139068767,2.422184467315674,2.5484588146209717,0.8673721663653851,0.06635940032217243,0.8275073692202568,0.22765260359796158,0.03813000023365021,0.029376539975852194,3.2416324028745294,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",170,34.081107766600326,2.2029106616973877,2.177109718322754,0.8437510393559933,0.06105164028136465,0.7549028731882572,0.20229864450216137,0.029573723673820496,0.021886176786875468,3.6117343239020556,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",220,44.12868115212768,2.017573118209839,2.002387046813965,0.8230604901909828,0.08270489410283853,0.7480338923633099,0.25625732936214746,0.0334029421210289,0.03663444312260983,3.7681214117910713,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",270,54.60853632190265,1.8605737686157227,1.8751516342163086,0.8107897005975246,0.07958135274580563,0.693975305184722,0.28959938838039,0.025918081402778625,0.030999802568253087,3.728540997952223,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",20,3.9644812527112663,5.458784580230713,5.494950294494629,0.9631653688848019,0.019543807638683547,1.377239216119051,0.2816966428371644,0.07458987087011337,0.05265749620348921,2.215755212120712,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",50,9.965992107987404,3.9584760665893555,3.968759775161743,0.9287450090050697,0.04863924044603716,1.0753483958542347,0.22274230902411898,0.05203435570001602,0.030289472354399365,2.565533713204786,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",100,20.140131381805986,3.005349636077881,2.932051420211792,0.8800952658057213,0.06527319871309885,0.860996913164854,0.27891670277678643,0.03582601249217987,0.03151918568736663,3.1609108559787273,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",150,30.162635686574504,2.473952293395996,2.4412646293640137,0.8694153614342213,0.07397326989353692,0.8731642551720142,0.25534050118229484,0.039183370769023895,0.030357686274702284,3.3061550869606435,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",160,32.13118995958939,2.404294967651367,2.3434934616088867,0.8505259640514851,0.08495777799284548,0.8061457797884941,0.27294473708349254,0.03724116086959839,0.03819280159798852,3.408045574091375,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",210,42.01827396452427,2.1485538482666016,2.0833547115325928,0.8496718667447567,0.08425706645965068,0.7757941093295813,0.2778263673150564,0.03170330077409744,0.03398251408623579,3.431914960965514,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",240,47.96283086552285,2.117807388305664,1.97972571849823,0.84814278408885,0.06829912838724513,0.7590938713401556,0.274777551772204,0.03278570622205734,0.03615119446584747,3.5660117079969496,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",320,63.7747947240714,2.0149810314178467,1.8361942768096924,0.8377351090312004,0.06628912938367036,0.7799388635903597,0.302684713653881,0.03657328337430954,0.03435971547669978,3.72820299398154,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",330,65.73918911907822,1.9465386867523193,1.8273900747299194,0.8182080127298832,0.08021408588559013,0.7287606131285429,0.28751928577533303,0.02614676207304001,0.028268128942228934,3.803120963042602,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",40,7.86303693219088,4.244988918304443,4.354681015014648,0.9331070743501186,0.04581814126284045,1.1052683293819427,0.2448369439437878,0.05511629953980446,0.03907088412957642,2.4005331369116902,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",90,17.759252797812223,2.8153533935546875,2.836425542831421,0.8767662048339844,0.06416306724158351,0.8957370854914188,0.26568181185434275,0.04323647916316986,0.04062274372963907,3.0355725979898125,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",140,27.669570881174877,2.3286960124969482,2.317831039428711,0.857080165296793,0.07002916699122075,0.8298551924526691,0.24316470863762005,0.034565411508083344,0.028152042412701226,3.443310782080516,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",180,35.596548832487315,2.1496920585632324,2.180509567260742,0.8421631455421448,0.08717644836375534,0.7613333240151405,0.22707768895795488,0.028424397110939026,0.025205326919395803,3.632722991053015,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",210,41.46205492829904,2.13102388381958,2.07051157951355,0.8351431265473366,0.07448418126637263,0.7606072202324867,0.208928828517773,0.028156399726867676,0.022765889145267205,3.566067487001419,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",260,51.213965869741514,1.9123859405517578,1.8860512971878052,0.822175070643425,0.0806538599577958,0.7596163265407085,0.2201767217075169,0.029942646622657776,0.02585619369063329,3.751589454943314,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",0,0.001465959008783102,7.128666877746582,7.165582656860352,0.9767150022089481,0.011793638009668376,1.9238969013094902,0.3343505773149601,0.1674973238259554,0.11483419696957549,1.149265293031931,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",10,1.9403859798330814,5.874649524688721,5.9510579109191895,0.9671616554260254,0.018422186148837272,1.564115695655346,0.37647820039737223,0.09710732102394104,0.07333256972330149,2.2139690809417516,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",60,11.640300699975342,3.5265603065490723,3.486762285232544,0.9054281711578369,0.04833438879763525,0.9591970220208168,0.1607868505770836,0.04769795015454292,0.03203899795314115,2.780250634998083,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",90,17.454365172656253,2.7773261070251465,2.735668659210205,0.8696296066045761,0.06029337841277676,0.855135153979063,0.2100780412895763,0.041312046349048615,0.03436058183742746,3.0903018838725984,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",170,32.516357406042516,2.2309048175811768,2.1320888996124268,0.8215033747255802,0.06694751609398052,0.6891531515866518,0.17525794929135538,0.02352076768875122,0.02064926650180147,3.415370380040258,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",180,34.37599152117036,2.15897274017334,2.093325614929199,0.8374324589967728,0.08383313375845071,0.7670064866542816,0.18109533417792023,0.033886346966028214,0.027142171606793556,3.522921304916963,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",230,43.67609175806865,2.0334811210632324,1.9199655055999756,0.8225404024124146,0.07888827399853737,0.7153851669281721,0.2108691312408566,0.031083405017852783,0.028110367829503397,3.593564657960087,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",280,52.968355803750455,1.8344073295593262,1.7721112966537476,0.805664025247097,0.07513975777878487,0.6552781295031309,0.1927792216673881,0.021021924912929535,0.016436926340549492,3.719001742079854,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",330,62.202002321137115,1.8210632801055908,1.7126500606536865,0.7962077297270298,0.07641443362931079,0.6983299106359482,0.2236408263003423,0.028137333691120148,0.03164849675466457,3.752155956113711,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",10,2.6099634011043236,5.724401950836182,5.796106815338135,0.9658196605741978,0.016714531687753824,1.4519239515066147,0.27653936176893773,0.07993724197149277,0.05740966031348441,2.132012318004854,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",50,12.897743597626686,2.9952263832092285,2.9974589347839355,0.8854403235018253,0.05257463860558239,0.8344933725893497,0.13870350718442112,0.03425378352403641,0.016047837757904027,2.87843400798738,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",100,25.945663298247382,2.430485486984253,2.3433237075805664,0.8452124521136284,0.060895017642464765,0.7678140848875046,0.17047125469693902,0.029675401747226715,0.024718956186908992,3.4666095699649304,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",150,39.16057229577564,2.1176087856292725,1.9766583442687988,0.8212270401418209,0.06848162494077517,0.6728618871420622,0.18167665731456353,0.022474780678749084,0.022525114644399217,3.6603687149472535,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",160,41.72652088000905,2.052614212036133,1.8795896768569946,0.8320880047976971,0.07604137811657197,0.7483360208570957,0.16035303320527325,0.031768813729286194,0.02413883636440725,3.639486258965917,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",240,62.354572436073795,1.9398643970489502,1.7446949481964111,0.8240912519395351,0.09541447879953446,0.7028968669474125,0.17055184662177317,0.025455310940742493,0.020872848361305738,3.9839392179856077,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",270,70.08287945215125,1.8673601150512695,1.6317775249481201,0.825285941362381,0.07876478240361726,0.6616085655987263,0.15909794707995772,0.020839236676692963,0.018682357185993603,4.132601612014696,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",320,82.86374262149911,1.6798741817474365,1.4737170934677124,0.8076466992497444,0.09612887675874748,0.691356185823679,0.18321845755870367,0.02827390283346176,0.03611258426426012,4.250610042014159,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",330,85.41119516675826,1.6426726579666138,1.4527560472488403,0.8065111190080643,0.08103271689553775,0.6761667467653751,0.15335386570029408,0.022138282656669617,0.02101071389201563,4.303709088009782,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",380,97.97452271834482,1.6299301385879517,1.3785364627838135,0.8160740062594414,0.08645863534433453,0.6535798609256744,0.1479522324318008,0.020395629107952118,0.016282776113222153,4.5876274650217965,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",430,110.60974258533679,1.5547846555709839,1.2985926866531372,0.7979397289454937,0.09293908283915622,0.6870849914848804,0.22633444808231729,0.024769440293312073,0.024949363580869324,4.436440971912816,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",20,5.092166144866496,4.803092002868652,4.801841735839844,0.9436637535691261,0.03709037260846278,1.1884500198066235,0.23978521913377293,0.059970248490571976,0.046604991122644455,2.2534968440886587,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",60,15.288089344860055,2.689333915710449,2.74057936668396,0.8638001717627048,0.07013039837136067,0.7687284462153912,0.19061640414471434,0.02474987506866455,0.02235016615805666,3.2474203900201246,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",110,27.990468330215663,2.1765124797821045,2.176290512084961,0.8476543724536896,0.08149006834726938,0.7597309164702892,0.19699961374702002,0.029528886079788208,0.02456594491364172,3.498775309068151,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",160,40.38212346681394,2.1509242057800293,1.971683382987976,0.8185438960790634,0.08196988298290105,0.7214159891009331,0.20866214621602563,0.02439277619123459,0.022784942391502895,3.6243558019632474,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",210,53.229186495766044,1.9795010089874268,1.7694060802459717,0.8217148408293724,0.0791126513934961,0.6809805929660797,0.11898196250816302,0.02125602960586548,0.016800510853341385,4.032820567023009,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",220,55.75913686968852,1.9776811599731445,1.7212237119674683,0.808319516479969,0.0951854005631965,0.6693443283438683,0.17522724073696305,0.019232235848903656,0.01646993686148589,3.898123290971853,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",300,75.8603622613009,1.8868687152862549,1.6132667064666748,0.7998329475522041,0.08664959098440955,0.6081241238862276,0.13035744761832163,0.016659609973430634,0.017309238871424024,4.159449046943337,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",20,5.052558567025699,4.889195442199707,4.968567848205566,0.952666524797678,0.031579780130410164,1.225847877562046,0.23599318989757495,0.05976206809282303,0.04450350282360216,2.2561985380016267,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",70,17.70592752203811,2.6428074836730957,2.6785404682159424,0.8553763665258884,0.06856727768887555,0.8194066509604454,0.2980844803647551,0.03304792195558548,0.04038494066306537,3.1970811360515654,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",80,20.23792143689934,2.5121679306030273,2.5640549659729004,0.8501891866326332,0.08305596115418067,0.8030087724328041,0.2376341853348989,0.0300833061337471,0.02671976163607157,3.4471348880324513,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",130,33.03192030300852,2.110966444015503,2.1723062992095947,0.8376146480441093,0.08952482744264159,0.7238285522907972,0.20769785474302377,0.024712078273296356,0.020145142144918825,4.870215057977475,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",180,46.04844365955796,1.9807586669921875,1.877808690071106,0.8114621415734291,0.10056557597041633,0.7868682183325291,0.24064511855324502,0.033736176788806915,0.02674405410452998,4.994167438009754,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",0,0.0018254038877785206,7.0903143882751465,7.10252571105957,0.9763477295637131,0.0113701762277954,1.9052208438515663,0.3209240491346589,0.16165009886026382,0.11136314109241303,1.116247306810692,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",40,10.725443253526464,3.3202102184295654,3.343496799468994,0.9035043083131313,0.054553462308509396,0.996784619987011,0.1785358130469251,0.049606576561927795,0.0320013841276166,3.4492158060893416,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",90,24.031428972491995,2.3900928497314453,2.368680000305176,0.8459805212914944,0.06460699470296433,0.8008865602314472,0.21345455223145074,0.03138880431652069,0.025143695801632154,4.271760690025985,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",140,37.034942778525874,2.136474132537842,2.035917043685913,0.829621035605669,0.07404282148973566,0.762039065361023,0.20740553319383745,0.027395762503147125,0.022960431902837215,4.681715817889199,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",20,5.389945637434721,5.060938835144043,5.125847816467285,0.9577041007578373,0.02579445991063041,1.2760465741157532,0.2337683858205557,0.059918902814388275,0.037182612918785204,3.12056543584913,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",60,15.810400772839785,2.853318929672241,2.9141952991485596,0.8804008923470974,0.05755247245883202,0.9081644006073475,0.2535373280231201,0.04505060613155365,0.04308011510615364,4.099702020874247,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",90,23.610246528405696,2.486459970474243,2.482217788696289,0.8435690179467201,0.07904710689531919,0.8204577974975109,0.22978746290029545,0.03617357462644577,0.029820424990576123,4.830924398964271,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",140,36.60284655401483,2.1389758586883545,2.0187594890594482,0.8255927562713623,0.0885500065079806,0.7828900963068008,0.29350451290537294,0.033367596566677094,0.036990608618030045,4.831385284895077,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",190,50.239485807484016,1.9695895910263062,1.8455475568771362,0.816618088632822,0.0986116958905253,0.7303760834038258,0.2055539301993635,0.029212720692157745,0.02177930017833714,6.037812302121893,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",200,53.15206477139145,1.840563178062439,1.8292639255523682,0.821928858757019,0.09175346898170242,0.6739947479218245,0.13858903828455543,0.020059041678905487,0.012503237485688157,6.18223633011803,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",250,67.69440891896375,1.8606032133102417,1.6757087707519531,0.8177777379751205,0.08233678351473958,0.7148692775517702,0.22616086149313677,0.02699597179889679,0.024750194556861702,6.556092997081578,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",20,5.8687421849463135,4.99395751953125,5.005908012390137,0.957123264670372,0.022748489707612746,1.3314376510679722,0.2914252848279799,0.07879771292209625,0.05820004573963037,3.3652622350491583,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",100,29.374180209590122,2.246795415878296,2.233116626739502,0.8294357098639011,0.08124059446286301,0.7407495230436325,0.10713721207472919,0.02489931881427765,0.01724169729989895,5.906916016945615,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",110,32.30429599597119,2.2836196422576904,2.237888813018799,0.8348362296819687,0.08676323448812416,0.7848745621740818,0.19088835242861008,0.030261866748332977,0.02572185988749136,5.565998321864754,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",160,46.920848693931475,1.8936800956726074,1.8880274295806885,0.8200623206794262,0.09467708831295803,0.7123055960983038,0.17192461590531088,0.022563956677913666,0.016628820954058706,5.945921402191743,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",210,61.72895838832483,1.806383728981018,1.7417783737182617,0.811554279178381,0.09226133440407192,0.7127146702259779,0.21138320020691082,0.026148416101932526,0.019223774291477102,6.223337204894051,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",260,76.28406892972998,1.7232375144958496,1.557382583618164,0.8005079366266727,0.10898815686078087,0.6736922822892666,0.2149782981104703,0.02344617247581482,0.024840344276048667,6.537630619946867,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",20,5.827608296647668,4.928192138671875,4.9926438331604,0.953950434923172,0.026944976856149147,1.2662594430148602,0.1735890572883903,0.06599429622292519,0.040580745575965645,3.518961082911119,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",60,17.47676595998928,2.9066760540008545,2.934783697128296,0.8882141821086407,0.060483050759898876,0.9568259231746197,0.2860763875373328,0.04878143593668938,0.04423352406443151,4.704347384860739,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",110,32.05741092516109,2.3160481452941895,2.286283493041992,0.8562948703765869,0.05861824236228874,0.780513983219862,0.1999727522359195,0.030856147408485413,0.024218397880309824,5.3768673439044505,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",160,46.67029065545648,2.0936150550842285,2.058800458908081,0.8378620520234108,0.07948418023914268,0.747409176081419,0.2608631037253205,0.029025301337242126,0.03025007591820406,5.972859377041459,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",170,49.60266323038377,2.00996994972229,1.9347493648529053,0.8360867574810982,0.09028984213390104,0.7294798344373703,0.2388670235719855,0.032338596880435944,0.04048551240619522,6.0385651481337845,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",220,64.19555886345915,1.9430639743804932,1.7668614387512207,0.8172598257660866,0.09440323625253211,0.6931287050247192,0.24547812248764048,0.025358907878398895,0.027768323166472753,6.26840211590752,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",250,72.96839220100082,1.8690911531448364,1.618648648262024,0.8028850145637989,0.0906802837594272,0.7236369885504246,0.26215514559999986,0.033336468040943146,0.03886712933033183,5.602382543962449,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",330,93.83909831685014,1.7210581302642822,1.5174956321716309,0.8175637312233448,0.081583460249243,0.6996101513504982,0.20153625321166396,0.02847249060869217,0.031193425625179853,6.011902676895261,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",340,96.46278946986422,1.7162001132965088,1.4267923831939697,0.8003810569643974,0.08023998006647307,0.7125764079391956,0.17226694694672662,0.029755771160125732,0.0282797797314499,6.152670077048242,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",30,7.861996730789542,4.160679817199707,4.191646099090576,0.9286476485431194,0.05365196316192965,1.089536339044571,0.26414794291709887,0.05679267272353172,0.03672707582625362,3.1816830011084676,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",80,20.966002756962553,2.5993168354034424,2.5197536945343018,0.8635889142751694,0.06487348080524354,0.8155053853988647,0.18425538127237692,0.03301553428173065,0.025473679213649975,4.669948576018214,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",130,34.161653937771916,2.122163772583008,2.040109872817993,0.8338627815246582,0.06999612598975526,0.7311538010835648,0.15765050765780284,0.024062365293502808,0.017839957186806164,4.8461697730235755,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",160,41.93622685386799,1.9550758600234985,1.9262981414794922,0.8330078646540642,0.07406367216804416,0.7090898379683495,0.20485179716394647,0.027118019759655,0.030161741754171197,4.858753344975412,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",200,52.32268529618159,1.9743846654891968,1.8608427047729492,0.8285491913557053,0.07950796662940765,0.6822461858391762,0.1882183392457161,0.022134147584438324,0.020188358430278545,5.046325791161507,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",250,65.25729658501223,1.777066707611084,1.6608604192733765,0.8002014867961407,0.08807347292753202,0.7024151794612408,0.20499350007242956,0.026664763689041138,0.02436392193122353,5.161716376896948,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",20,5.193566726753488,4.543310642242432,4.620400428771973,0.9411631524562836,0.0385034372386663,1.1556189619004726,0.2294953573143088,0.058915507048368454,0.045296999959068635,2.946140981046483,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",30,7.786656924756244,3.6228044033050537,3.6760594844818115,0.916779700666666,0.04803821929518682,0.9587348327040672,0.18124823451310446,0.0406971275806427,0.03247200412001584,3.2083619120530784,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",110,28.45670615672134,2.2252886295318604,2.2341630458831787,0.8389586433768272,0.06192025234854881,0.7173899505287409,0.15249807452490832,0.022637739777565002,0.016459225243939665,4.728865711949766,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",140,36.191311944508925,2.0884389877319336,2.057004451751709,0.817593477666378,0.08514979684815284,0.711077818647027,0.22140968631755598,0.02925003319978714,0.027530608766034192,4.640692302025855,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",190,49.06021490460262,1.9429035186767578,1.800873041152954,0.8109720312058926,0.09033617115916553,0.6626383140683174,0.16970666517410593,0.01815751940011978,0.013203045613714024,5.05092042312026,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",200,51.63067765859887,1.891550064086914,1.8079774379730225,0.8115513771772385,0.07904992810856232,0.7154145315289497,0.19876719130413278,0.0282023623585701,0.028913762733882277,5.231761519098654,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",250,64.36297354847193,1.765733242034912,1.615806221961975,0.794645395129919,0.08338919942169284,0.6440954189747572,0.17462642256263727,0.023733392357826233,0.022667234482322593,5.039530375041068,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",300,77.05521771265194,1.5899823904037476,1.5037227869033813,0.7958961427211761,0.0868716995008043,0.6578720677644014,0.1844718489415749,0.02204592525959015,0.021340218579365106,5.293358925962821,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",330,84.64081696094945,1.7129015922546387,1.4741439819335938,0.7849409617483616,0.08484631845890536,0.6493052635341883,0.20895261770441348,0.0209466814994812,0.020617382642737277,5.542619659099728,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",20,5.086211445741355,5.112459659576416,5.202175617218018,0.9598700031638145,0.023934849350500876,1.375484500080347,0.28300371454562134,0.0745878741145134,0.04819469372573656,2.803033048985526,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",70,17.643385195871815,2.6419517993927,2.6782326698303223,0.8685608096420765,0.06899541887290027,0.7851211242377758,0.2147287344250542,0.03144267201423645,0.027165695043767132,4.094924285076559,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",120,30.02256042510271,2.1742470264434814,2.184014320373535,0.8361823856830597,0.07391303631961199,0.7601072117686272,0.27719181938144244,0.03127268701791763,0.0335042619018586,4.577659863047302,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",170,42.12983085424639,2.150829315185547,1.9936050176620483,0.8206455372273922,0.08575559570453552,0.682427667081356,0.13255851911580593,0.02348683774471283,0.023488670136406275,4.599993747891858,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",180,44.55513204052113,2.131948947906494,1.973586082458496,0.8320642225444317,0.07635601313946104,0.7021996080875397,0.19142279493920006,0.023953355848789215,0.019682926837783585,4.978518285090104,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",260,65.32837359723635,1.831061601638794,1.6644957065582275,0.8150632940232754,0.09229337750872141,0.6646410506218672,0.1830376648425551,0.023008190095424652,0.02248095594773036,5.406314454041421,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,4.1086452540475875,4.67841911315918,4.661909103393555,0.9537975527346134,0.02196786712213463,1.1582285799086094,0.30634592110143033,0.04459269344806671,0.04882309705210635,0.04554643400479108,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",70,14.4594571251655,4.271200180053711,4.043335437774658,0.9285869747400284,0.037750167563108006,1.0448933653533459,0.3365309849943828,0.0332566574215889,0.03931208863884852,0.04618834599386901,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",80,16.546786506427452,4.1798553466796875,3.9609313011169434,0.9247162938117981,0.03707254379128921,1.029081966727972,0.3338509270542509,0.035759277641773224,0.043108823525972895,0.04402373603079468,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",130,27.213884589495137,4.009241580963135,3.801920175552368,0.912807784974575,0.04572236702660783,1.0088153518736362,0.31773735004108883,0.03151912987232208,0.03305150749514241,0.05732871894724667,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",180,38.20359978626948,3.9325599670410156,3.710451364517212,0.9177581444382668,0.04075974808421882,0.9845195859670639,0.2824875569370962,0.02577832341194153,0.02357202576238412,0.051022866973653436,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",210,44.76633590168785,3.959728717803955,3.664036750793457,0.9146135784685612,0.045385427240707564,0.9777270220220089,0.2862486295064485,0.025983721017837524,0.022518214797692664,0.056131146964617074,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,6.476353285019286,4.271039009094238,4.290282249450684,0.946440827101469,0.02312990781575732,1.1100763343274593,0.33711754529958016,0.04095764458179474,0.03565924940355153,0.05680607305839658,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",80,17.154890544130467,3.9309215545654297,3.859011650085449,0.9188242480158806,0.039333957161859924,1.019009131938219,0.3314251821467405,0.029566846787929535,0.030086328028399088,0.05293204099871218,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",130,27.810221055988222,3.7929670810699463,3.710010051727295,0.9154348857700825,0.04835558318738981,1.0083124823868275,0.34688125168770845,0.031197957694530487,0.033162821084177875,0.05876171006821096,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,2.185193683835678,4.924266815185547,4.901183128356934,0.9618210643529892,0.016617353731094884,1.2321690954267979,0.31519609950214467,0.052924931049346924,0.04293321247282595,0.06052001204807311,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,4.285616752924398,4.479905128479004,4.436736106872559,0.9545902498066425,0.01826054379509065,1.1802014857530594,0.3522806572827572,0.053383540362119675,0.049843596357541764,0.054839818039909005,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",90,19.224370610434562,3.90433931350708,3.769780397415161,0.9155907519161701,0.04724682074131446,1.032752025872469,0.37664124639868557,0.03588264435529709,0.04589494966581638,0.0569594589760527,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,4.875045263906941,4.923979759216309,4.989731788635254,0.9586775451898575,0.01842606866096387,1.2943150736391544,0.4068350379782395,0.07657500356435776,0.07313423872109617,0.08990610297769308,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,18.017112009227276,4.115398406982422,3.9484944343566895,0.9333185516297817,0.0265327109560925,1.0483819507062435,0.35428448210979074,0.03590432554483414,0.043026856951396755,0.08111791405826807,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",70,20.2107534611132,4.049617767333984,3.8913960456848145,0.9228816628456116,0.036942238880187495,1.0064503159373999,0.3473228525119625,0.03027864545583725,0.038252812558750425,0.06635189603548497,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",120,30.603474933304824,3.886319160461426,3.7186636924743652,0.9128434099256992,0.042106942053369865,1.019764894619584,0.37527959538255723,0.033746764063835144,0.04716309270065839,0.05244003003463149,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",150,37.01816021546256,3.882185697555542,3.65649676322937,0.911559421569109,0.047168391766968715,1.0172400567680597,0.3538675745751945,0.03258349746465683,0.040829203997627184,0.057391991023905575,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,8.48170895408839,4.026899814605713,4.023974418640137,0.9314569272100925,0.02364298952178595,1.0816291868686676,0.37794891079747334,0.040716804563999176,0.053568297406797465,0.049400098039768636,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,10.52511091704946,3.938948154449463,3.934570074081421,0.928497601300478,0.02668635177196824,1.0418160948902369,0.3659039606225492,0.03674238920211792,0.0513411349719023,0.04994871804956347,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",100,21.490233664633706,3.726414203643799,3.7022085189819336,0.9136361740529537,0.044651831898119804,1.010151818394661,0.3497714074883333,0.028502564877271652,0.039943527793887934,0.07359508599620312,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",150,32.594694955740124,3.681281566619873,3.6101059913635254,0.9115265496075153,0.04621995409759373,1.0057000331580639,0.332948485428806,0.028836596757173538,0.03808511149782054,0.051222858019173145,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",200,43.61147546826396,3.5985805988311768,3.555522918701172,0.9170825593173504,0.03659804227646345,1.000858586281538,0.3440757887699617,0.029015984386205673,0.03893349357897928,0.06314324703998864,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.003334679058752954,7.1336989402771,7.124753952026367,0.9776327647268772,0.011646374700180397,1.986688680946827,0.2972866426386461,0.16614506021142006,0.10332375292807265,0.050940515007823706,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,8.550465345964767,4.38815975189209,4.2129669189453125,0.9417730644345284,0.01919306913388114,1.0937711261212826,0.37233477464283926,0.03940626233816147,0.04016364893260592,0.04528545995708555,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",90,18.957573309307918,4.00832462310791,3.861656427383423,0.9193421639502048,0.044772712185093246,1.0242918655276299,0.3568850473249694,0.03137723356485367,0.032899275070666645,0.048402859014458954,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",140,29.375611912459135,3.9055957794189453,3.7229816913604736,0.916596919298172,0.04753903248076955,1.0072533208876848,0.39407372749669906,0.029400020837783813,0.03931062809276363,0.04200625605881214,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",150,31.291545088402927,3.925938129425049,3.704045295715332,0.9147335924208164,0.051791617718825685,0.9743924327194691,0.37029592949816537,0.022386372089385986,0.03261054308989893,0.040280458983033895,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,6.48987509123981,4.281160831451416,4.174708366394043,0.940305694937706,0.02084489053746253,1.0649433992803097,0.319246909213175,0.03400029242038727,0.03698273187327724,0.04680780600756407,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,12.991236009867862,4.062019348144531,3.878957509994507,0.9276432730257511,0.02696206297684924,1.0258065909147263,0.31598047009780206,0.029811188578605652,0.030770921553784913,0.04613287793472409,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",140,30.215139768319204,3.7765393257141113,3.612051248550415,0.9124752506613731,0.04141890914533314,0.9968352429568768,0.34429145112803244,0.02847776561975479,0.03650211574584209,0.04584687505848706,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",150,32.354388296138495,3.749032735824585,3.589891195297241,0.9135452322661877,0.03342842848675029,0.9807725548744202,0.33163092208124917,0.026959829032421112,0.03360065400705107,0.05054242187179625,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.00383983482606709,7.17506742477417,7.149613380432129,0.9766230955719948,0.012807359335720796,1.9407529532909393,0.313590142269943,0.16348119638860226,0.10718562471705809,0.05296287685632706,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,10.555462470510975,4.226012706756592,4.047079086303711,0.939267847687006,0.023729773614238742,1.051710568368435,0.30958082349046934,0.0349002331495285,0.03177479263691083,0.04488040693104267,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",100,21.477347179083154,3.950829267501831,3.783902168273926,0.9192798510193825,0.047475380223543764,1.0093441847711802,0.2908346660458914,0.028974130749702454,0.029292023047619943,0.052852826192975044,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",130,28.110882634529844,3.815964937210083,3.6991069316864014,0.917387742549181,0.04213500368375934,1.0069166496396065,0.2938703586481878,0.0300556942820549,0.027552288331232737,0.057201530085876584,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",170,36.833142559276894,3.7701056003570557,3.6261065006256104,0.9119261018931866,0.050553144549013086,1.0051781795918941,0.32421944879039,0.030391894280910492,0.033389916345961675,0.047156512970104814,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",220,48.029652528930455,3.665225028991699,3.577502727508545,0.9109760597348213,0.04938717054960281,0.9922607280313969,0.3274057145977691,0.02880960702896118,0.03413048299876783,0.053894670912995934,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.0034724571742117405,7.217339992523193,7.192926406860352,0.9777523279190063,0.010370182681525807,1.875240571796894,0.3179643132180702,0.16353943943977356,0.1045890756743382,0.05266702896915376,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,2.2039911313913763,4.894720554351807,4.929592132568359,0.9600500129163265,0.01759029147516255,1.2077580131590366,0.32550386087799915,0.049390509724617004,0.04508799885302263,0.05525034898892045,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,12.849858848378062,4.021679401397705,3.9489917755126953,0.9340199157595634,0.026229282735486855,1.0359863005578518,0.296371613646494,0.035889483988285065,0.03847355033309781,0.054493827978149056,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",130,27.635577391134575,3.7945518493652344,3.661515235900879,0.9150384701788425,0.04992617770774071,0.970084223896265,0.28588758845771245,0.0246763676404953,0.026812317339935766,0.04832954588346183,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",170,36.14564813557081,3.7006125450134277,3.594686508178711,0.9159843325614929,0.052816454706163946,0.9580028913915157,0.2628972847676057,0.02504931390285492,0.021706641469354568,0.05389637500047684,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",180,38.261065734317526,3.747274875640869,3.5777971744537354,0.913665097206831,0.05148189389809808,0.9787827506661415,0.2970975815476991,0.026416011154651642,0.02455597611804345,0.05402103811502457,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",230,48.78014186886139,3.684525728225708,3.531741142272949,0.9171776063740253,0.05758144696888495,0.9619875364005566,0.2897502740291761,0.022015169262886047,0.018442993354663463,0.05453596613369882,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,4.2627250829245895,4.550265789031982,4.530234336853027,0.9526367038488388,0.020094773891475542,1.1446261182427406,0.2650848691908027,0.045476820319890976,0.03780110451921464,0.054085373878479004,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,10.66763758403249,4.102433681488037,4.0169548988342285,0.93035988509655,0.026257120112455287,1.0582714639604092,0.35827209486218614,0.0385378897190094,0.04452603813918527,0.05231033405289054,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",90,19.0355819221586,3.884571075439453,3.8050622940063477,0.9175700061023235,0.04172879032869519,1.0275124739855528,0.3587297742769108,0.036189965903759,0.04362721985519798,0.05133642698638141,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.02077161008492112,7.319734573364258,7.293687343597412,0.9787299521267414,0.011988306029935765,1.9567811414599419,0.29047052326970785,0.1614220328629017,0.10171484637615186,0.14164937299210578,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,27.87559504667297,2.388943672180176,2.102900981903076,0.8287362791597843,0.06250109864068783,0.8378380089998245,0.25880090311254783,0.03635992854833603,0.03303517782583538,0.16101016197353601,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",100,54.136898106196895,2.206937313079834,1.7098811864852905,0.8172253854572773,0.06672017218738577,0.7912528775632381,0.22127184337593067,0.03507121652364731,0.030017898393289635,0.13708693301305175,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",110,59.669928496237844,2.1972477436065674,1.5773404836654663,0.8036835044622421,0.07417390235737258,0.7670376189053059,0.23636461611763443,0.031014643609523773,0.03256007258008873,0.14989328698720783,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",180,96.21409720683005,2.211519241333008,1.4078607559204102,0.7976997755467892,0.0851881976744069,0.7280347272753716,0.25587672352631124,0.02712322026491165,0.02842224668829499,0.14493158599361777,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,10.400895830942318,2.9081530570983887,2.772352695465088,0.8766808584332466,0.052097018806872716,0.9853505417704582,0.3151151442218625,0.042857080698013306,0.03693748395161437,0.1513370539760217,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",70,36.0262046137359,2.4506607055664062,1.8658933639526367,0.8204334639012814,0.07825005657408125,0.7883292660117149,0.2565645748548286,0.030279450118541718,0.029293512052188588,0.1462296659592539,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",80,41.1562926286133,2.4073853492736816,1.7220449447631836,0.8151250146329403,0.07947043096892187,0.7735922038555145,0.2782436560306832,0.02591918408870697,0.024880652627576147,0.1407309799687937,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",130,67.38023279537447,2.210010051727295,1.437290906906128,0.8159440010786057,0.07604189281529829,0.8142836578190327,0.27743423853168836,0.03419321030378342,0.02757967739993584,0.14684109902009368,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,5.123085568076931,4.131681442260742,3.9669666290283203,0.9303288757801056,0.03255848478881407,1.1839556135237217,0.36562264444173875,0.05745740234851837,0.04972133363921112,0.1416623879922554,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,20.81565029511694,2.5332694053649902,2.2923574447631836,0.8458596244454384,0.07212959526763348,0.8504517637193203,0.3812122397464227,0.038011617958545685,0.04239638746816567,0.14401023497339338,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",80,41.718493229942396,2.234149694442749,1.809026837348938,0.823305282741785,0.08420433649824655,0.7347431425005198,0.30169435222144714,0.027380459010601044,0.02996160832467019,0.14644400193355978,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",130,67.7076971422648,2.0307562351226807,1.4513514041900635,0.8056967444717884,0.07729732978277148,0.7428423520177603,0.3188928101178431,0.02773832529783249,0.029840018538109133,0.1507103469921276,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.007116468041203916,7.267636775970459,7.205214977264404,0.9770204722881317,0.01160023891164876,1.9437237977981567,0.31331186145274403,0.1730489507317543,0.10162568441100352,0.16764252807479352,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,28.622374086407945,3.187401294708252,2.724336624145508,0.8722214326262474,0.059904183163083796,0.8722739350050688,0.2944070113082182,0.031312696635723114,0.03126978532104894,0.16783619101624936,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,34.05626912438311,3.2586452960968018,2.6618094444274902,0.8714621365070343,0.055098592641980436,0.8470536675304174,0.2980769150809617,0.026882261037826538,0.026232623586249792,0.1437250009039417,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,32.33854896586854,2.560861349105835,2.2352054119110107,0.845977995544672,0.05057530513903869,0.8076233081519604,0.3369791902811119,0.03373803198337555,0.038870002103991996,0.17721509700641036,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",90,53.284383860533126,2.207313299179077,1.8256970643997192,0.8284885846078396,0.06330994710094971,0.7953975722193718,0.32282165273892505,0.03397649526596069,0.038049998628417694,0.14285743702203035,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",140,78.96106598596089,1.9826886653900146,1.5416946411132812,0.8118882328271866,0.07101379206048807,0.7348962016403675,0.33240223443090855,0.030146010220050812,0.04213460140437302,0.1334236350376159,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",150,84.0140360557707,1.9127286672592163,1.5037720203399658,0.8013323657214642,0.06801132537217816,0.7597491424530745,0.31625406036819353,0.03163775056600571,0.03878522308150007,0.13583066104911268,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",200,109.38389242894482,1.9871822595596313,1.4197258949279785,0.7914693020284176,0.07373601664125308,0.726635629311204,0.3280750868275806,0.030568376183509827,0.038796893991155906,0.13664944097399712,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,20.776376251364127,2.5940074920654297,2.2272722721099854,0.8325178101658821,0.06894093202842314,0.8449251018464565,0.3385386008333483,0.03470654785633087,0.035955031218297726,0.13632723700720817,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,31.042450173408724,2.3255085945129395,1.9948692321777344,0.8226638399064541,0.07212730316784666,0.8200407214462757,0.32730715693090195,0.03220465034246445,0.03102437815461528,0.14320674492046237,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",110,57.49230283347424,2.161076545715332,1.7301030158996582,0.825225193053484,0.07302431215106449,0.7799904998391867,0.31885994460636863,0.033414967358112335,0.03836922291949075,0.13513314898591489,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",160,83.37160240486264,2.1440882682800293,1.5756176710128784,0.8273319080471992,0.07735725196665996,0.7782964743673801,0.2943962480336516,0.031581439077854156,0.024951874585948827,0.1333734600339085,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",210,109.71424879156984,2.2240347862243652,1.4627346992492676,0.8009343110024929,0.06611356933044735,0.692298699170351,0.2572911070818938,0.022297091782093048,0.023690110760789296,0.13607152504846454,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",260,136.50928828888573,2.1358659267425537,1.4171135425567627,0.8137513399124146,0.07997089476004028,0.7286241520196199,0.2470469925710324,0.026617832481861115,0.024050654325110655,0.14664585585705936,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",290,153.31714167748578,1.9942059516906738,1.3311388492584229,0.8269353471696377,0.0792068308179194,0.745366670191288,0.2784228232625989,0.02896925061941147,0.03093010333374142,0.15455415309406817,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,5.429475007811561,4.1434831619262695,4.10059118270874,0.9406422525644302,0.020411365349931446,1.159412033855915,0.3410001904738946,0.04380277544260025,0.036290035666921794,0.1450704550370574,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,32.57691245037131,2.459487199783325,2.049318552017212,0.8301724195480347,0.047600639045804714,0.8768955618143082,0.2689138763997056,0.03263995051383972,0.031049479742718687,0.13661738391965628,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",110,58.18420909671113,2.3146910667419434,1.5412801504135132,0.8022430501878262,0.05456195139779735,0.8331481106579304,0.24767650370470118,0.030716150999069214,0.026360070097558395,0.14521868294104934,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",120,63.410666001727805,2.164834499359131,1.556410789489746,0.8031618595123291,0.056805340798912154,0.8438601829111576,0.2350977509717618,0.03229740262031555,0.02734951074175897,0.14830173389054835,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",170,89.16473217913881,2.2013015747070312,1.3822678327560425,0.8140250593423843,0.05514479221214467,0.8271084502339363,0.20486703259312808,0.029130972921848297,0.020195790393641062,0.14405096997506917,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.0056891790591180325,7.276558876037598,7.278131484985352,0.9804681278765202,0.009966362040297506,2.0623988658189774,0.31967852657174933,0.17803462781012058,0.11066785873581267,0.14588248706422746,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",80,48.47932884655893,2.224276065826416,1.9333012104034424,0.8232709132134914,0.06617655209343101,0.7497255466878414,0.2547472683426715,0.028873935341835022,0.0301997346585166,0.1568106128834188,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",90,54.26970566948876,2.1547317504882812,1.8482286930084229,0.828186459839344,0.07339509024994781,0.7437785435467958,0.2701194400910693,0.02677793800830841,0.02887960955172958,0.1560981390066445,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",140,85.92917883722112,2.0668187141418457,1.620843529701233,0.800597682595253,0.06387911477523255,0.752351600676775,0.2862649305971737,0.03138834238052368,0.03241541555211165,0.15287405299022794,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,18.834906904725358,2.850593090057373,2.576078414916992,0.8761258125305176,0.04623178999186884,0.9253956135362387,0.3053796634197947,0.03652206063270569,0.03200042498938809,0.1547711759340018,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",80,50.08208952774294,2.291627883911133,1.878406286239624,0.8244327381253242,0.06689937928299437,0.7722076680511236,0.32633343637797946,0.029170945286750793,0.03259584789409056,0.16349517204798758,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",110,68.26776456227526,2.1598591804504395,1.6698722839355469,0.8117961660027504,0.07004320087008757,0.7224627565592527,0.36413488823905227,0.02625507116317749,0.03941246809043997,0.1503199019934982,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",150,92.01423561922275,2.040879487991333,1.5199880599975586,0.8027647770941257,0.07221501919891536,0.7327353525906801,0.35026258826976125,0.027909167110919952,0.036606145069889864,0.15914953802712262,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",200,122.24560085893609,2.0577988624572754,1.4402369260787964,0.8019068650901318,0.08161821618359263,0.7313065249472857,0.3839796851898325,0.028237879276275635,0.04171430672254937,0.170918689109385,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,18.816731074359268,2.8103079795837402,2.650505542755127,0.8518077954649925,0.05302612548957403,0.8567228317260742,0.35674467821576655,0.03762707859277725,0.039168615282066575,0.16310197417624295,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,25.54449917911552,2.5952463150024414,2.3489134311676025,0.839874017983675,0.05408475546252127,0.8247603010386229,0.34459119892648093,0.036266155540943146,0.03951184915137258,0.1675279620103538,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",90,57.35342620848678,2.3053033351898193,1.7483367919921875,0.8072500601410866,0.06692187591670452,0.7810394316911697,0.3402725361628067,0.036912545561790466,0.039608548287692585,0.1616549980826676,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",160,97.09099088259973,2.22153902053833,1.4338282346725464,0.8136969283223152,0.06655161896035172,0.7444543242454529,0.25591273300387557,0.0284629687666893,0.02182491267997609,0.14306214987300336,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",200,117.80729423020966,2.0955142974853516,1.3071810007095337,0.8023091778159142,0.06089404505523789,0.7523775603622198,0.2355952455470061,0.029769331216812134,0.020074126286322685,0.14500925014726818,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",210,123.03049768903293,2.023639678955078,1.2609587907791138,0.8109162785112858,0.07880318750551764,0.7425970062613487,0.24338671691245542,0.028306804597377777,0.019572307638748628,0.14108273293823004,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,25.727002377971075,2.668914794921875,2.4137539863586426,0.8745768964290619,0.05654069825059477,1.1111624129116535,0.4181710009870341,0.040039464831352234,0.0338118775737195,0.24710224603768438,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",80,68.78428175416775,2.3351030349731445,1.8701411485671997,0.8345648236572742,0.049969188479002453,1.0223246570676565,0.4002508875271472,0.03421419858932495,0.02709649348972438,0.23159552598372102,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",130,109.6238264888525,2.3145575523376465,1.5799446105957031,0.8419445902109146,0.07005210148554902,0.9375534504652023,0.5304690211011863,0.03091992810368538,0.022477308902224404,0.24565709999296814,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",150,126.00204066326842,2.0932016372680664,1.2081456184387207,0.8188175372779369,0.05781932194737097,0.8641060069203377,0.33393503444836486,0.030594095587730408,0.018732817769440958,0.2404058329993859,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",200,166.65502355189528,2.406353712081909,1.1375970840454102,0.8086550347507,0.0696255040664986,0.861703559756279,0.27620567009997343,0.034269049763679504,0.022229218242885086,0.2351111740572378,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,24.259382623131387,2.5147767066955566,2.0951762199401855,0.864900030195713,0.049739844964117916,0.9461386948823929,0.3243148716609418,0.04030858725309372,0.04026572783961919,0.23304030299186707,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",80,64.5667081575375,2.197324514389038,1.489977240562439,0.821689423173666,0.05587078991997746,0.8349317461252213,0.2763071808558803,0.027356311678886414,0.0253760343186652,0.24171412701252848,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",90,72.5794199850643,2.1815872192382812,1.4081836938858032,0.8303210511803627,0.060205942642504624,0.8101669624447823,0.2706700693518885,0.02650579810142517,0.02656044401395836,0.23702591296751052,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,48.56089461024385,2.0752952098846436,1.6310718059539795,0.8428042829036713,0.05542852557804048,0.8058431390672922,0.27830795258297936,0.02433648705482483,0.023837970157627966,0.2355531130451709,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",100,80.75200782122556,2.30727219581604,1.3620598316192627,0.8233307786285877,0.05864211741223717,0.7755615431815386,0.2970793030717362,0.025326251983642578,0.028257320261850735,0.235617958009243,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,24.131179469055496,2.543694257736206,2.005138874053955,0.8621260225772858,0.049475443841166686,0.8827276527881622,0.31500001341770495,0.03315107524394989,0.033045366880392696,0.23649311403278261,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,32.176540838205256,2.321410894393921,1.8163117170333862,0.8487865291535854,0.05854437395413184,0.8423047736287117,0.29490527453648124,0.0320875346660614,0.03319013126733189,0.23415131797082722,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",90,72.42941237310879,1.9572372436523438,1.3060272932052612,0.825746912509203,0.06946103046139898,0.7968918811529875,0.3263379983219099,0.02950400859117508,0.033940912569051136,0.2374259529169649,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,8.333652420085855,3.2802300453186035,3.2283859252929688,0.9065857455134392,0.03381528185806572,1.0934805385768414,0.33808035445728285,0.048139430582523346,0.04455091680094885,0.2486572259804234,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,33.51384400797542,1.872793197631836,1.515604019165039,0.7849722690880299,0.057337213663893064,0.7437380868941545,0.27030757689772406,0.030405715107917786,0.030565259445762945,0.2502048179740086,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",80,67.86092420492787,1.7876930236816406,1.0479544401168823,0.7695287466049194,0.08626203598141333,0.713183181360364,0.2723060161924351,0.030702106654644012,0.035907688731257784,0.2554072439670563,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",130,110.29319191281684,1.8892467021942139,0.8284012675285339,0.7567723914980888,0.07020984339490918,0.6658991556614637,0.15898276723413005,0.021478883922100067,0.013286399578699447,0.24932579195592552,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,16.041588516905904,2.7052769660949707,2.512803792953491,0.8795755542814732,0.049616422256491655,1.0313838720321655,0.33737609213562914,0.045205049216747284,0.036618383774467966,0.22608274000231177,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",70,56.63917593995575,2.059353828430176,1.4871917963027954,0.8158861584961414,0.05776269839865718,0.819703470915556,0.27696821381438597,0.03644078969955444,0.03097402588080597,0.2297654920257628,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",80,64.83192112413235,2.1195597648620605,1.3380742073059082,0.7931518033146858,0.0573221557839344,0.8239852413535118,0.29300926389349846,0.03861122578382492,0.03279277317969347,0.2310651099542156,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.006830101017840207,7.255087852478027,7.27329158782959,0.978576548397541,0.013550209455739985,1.9637742936611176,0.32466240154182135,0.18146050535142422,0.11213714657341027,0.23197868699207902,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,32.26861931558233,2.102786064147949,1.647974967956543,0.7948941551148891,0.08435492680746119,0.7369068898260593,0.2316652767837248,0.02582807093858719,0.023109083486554556,0.2310113109415397,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",90,72.7551597354468,1.764029622077942,1.0117928981781006,0.7727626860141754,0.08255223546980878,0.6734891775995493,0.24372509849358623,0.023269884288311005,0.02436420803378444,0.23756308003794402,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",100,80.82154858508147,1.9262726306915283,1.0393452644348145,0.7871732041239738,0.07706956849548764,0.6743558403104544,0.23996677322232007,0.024024181067943573,0.023182284269824976,0.23178381100296974,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,16.073783621774055,2.7886178493499756,2.6302402019500732,0.8882162794470787,0.0480001272478308,1.11157563701272,0.3560866808633193,0.04816810041666031,0.041942789893346,0.23051809694152325,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",70,56.63094571197871,2.718043327331543,2.013397693634033,0.8703639209270477,0.047524129211304514,0.9637922178953886,0.340300111084129,0.03895891457796097,0.03563931863713545,0.22134974179789424,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",90,72.77631161769386,2.3539371490478516,1.7030901908874512,0.826263789087534,0.060703518103724,0.8869331870228052,0.2864646185131772,0.03577472269535065,0.03479544862874138,0.2361344820819795,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",140,113.7474655128317,2.089212656021118,1.2416099309921265,0.8126502744853497,0.06032873020470625,0.8638898096978664,0.2981729992348088,0.034022994339466095,0.029260335629960395,0.2378163819666952,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,9.02892632689327,3.2547144889831543,3.1403026580810547,0.9086002446711063,0.04291463461698885,1.2083204127848148,0.4301761297079454,0.048770323395729065,0.0415002932970085,0.25335042807273567,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,51.84600466140546,2.52558970451355,1.9460184574127197,0.8579688742756844,0.05114927204060231,1.0035058055073023,0.31526793059212305,0.042616017162799835,0.030525863436603327,0.2361044199205935,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",110,92.36070820735767,2.497438907623291,1.642970085144043,0.8551003336906433,0.05605584146918273,0.959560938179493,0.3417492440403364,0.03439612686634064,0.02493897285631085,0.24297205987386405,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",120,100.41851288895123,2.2232537269592285,1.6385037899017334,0.8446687385439873,0.04752015888829608,0.9299873542040586,0.3359803082460875,0.032658085227012634,0.026906816147781593,0.24308911780826747,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,16.13656492321752,2.6117420196533203,2.422025442123413,0.8771299012005329,0.03620217761691707,0.9601378925144672,0.28191684161987546,0.04105089604854584,0.03796729366508455,0.22493896400555968,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",70,56.27328397310339,1.9701554775238037,1.3239363431930542,0.7995880022644997,0.04581207401606923,0.725531442090869,0.21320826645952093,0.024470917880535126,0.018266621983786757,0.23366423905827105,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",120,96.29707633703947,1.707249402999878,0.9232398867607117,0.765192911028862,0.05345113416213345,0.6984439734369516,0.22153862686274292,0.021334469318389893,0.016386705632762382,0.22884359606541693,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",130,104.49582366412506,1.784325361251831,0.8829169273376465,0.7783511243760586,0.06703450007057714,0.6953655779361725,0.236518157006517,0.024582989513874054,0.022086040375765528,0.22414286085404456,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,9.49315537000075,1.8997859954833984,1.9152225255966187,0.8236059881746769,0.07289551758667127,0.7886049151420593,0.3157697845687701,0.03685867786407471,0.0392118133716816,0.17590066301636398,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",90,42.63335294241551,1.1895411014556885,1.1209728717803955,0.7542407773435116,0.0901155241098944,0.7023524567484856,0.34465192970343744,0.03206504136323929,0.04177944737597896,0.17634926398750395,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",130,61.587695911643095,1.04766047000885,0.9855712056159973,0.7448117770254612,0.09506020772435038,0.6965711861848831,0.3517159762495488,0.03462841361761093,0.044169239255636865,0.1779371410375461,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",140,66.33813481871039,1.0187649726867676,0.9302477240562439,0.7461801469326019,0.09261656185374553,0.7002036739140749,0.35683986453855304,0.03361625224351883,0.043472830285441244,0.17559626104775816,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",190,90.06697027292103,1.0495449304580688,0.8902478218078613,0.7370949350297451,0.10009420727501347,0.7230676468461752,0.3802079963792106,0.036912620067596436,0.05107043460785105,0.17920404905453324,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,14.246141588897444,1.8437312841415405,1.8161768913269043,0.7920264378190041,0.09966053464687326,0.7657948955893517,0.3146349310975906,0.03238849341869354,0.03947669488920882,0.17203958798199892,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",80,37.96459529956337,1.386228084564209,1.2429999113082886,0.7650759629905224,0.09993443169390707,0.7436775229871273,0.3234333135094352,0.03683488816022873,0.04239664985751011,0.1853120269952342,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",110,52.18184913136065,1.215360403060913,1.071914792060852,0.7579328641295433,0.09257903972577573,0.7295267209410667,0.3595687311447857,0.03783586993813515,0.048611954303645115,0.1799170731101185,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",150,71.14036531443708,1.1966155767440796,1.0183519124984741,0.7552188523113728,0.1035774377096042,0.7329033426940441,0.36460372467342034,0.03554677590727806,0.04931022509062932,0.17279226309619844,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",200,94.80405807029456,1.0381803512573242,0.8637735843658447,0.7431002147495747,0.09822723521384001,0.683832436800003,0.36663310815528594,0.03239526227116585,0.04973820430701308,0.17782414285466075,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,14.543049884960055,1.5799415111541748,1.5794999599456787,0.8078958839178085,0.09390137087360988,0.8141310065984726,0.3030513427199887,0.04143153131008148,0.04256472027014988,0.18425173102878034,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,19.26993372431025,1.550100564956665,1.4626861810684204,0.7906475514173508,0.0885693557386616,0.7485027499496937,0.32160114508408144,0.03471754491329193,0.04214534077056945,0.17439506389200687,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",90,42.87091603083536,1.3346853256225586,1.164652705192566,0.7647699601948261,0.11407019661110002,0.7093781009316444,0.32932153596865227,0.03547065705060959,0.04295912524988376,0.176362524041906,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",160,78.08404883276671,1.1482603549957275,0.9160205125808716,0.74969332665205,0.10049501969881397,0.6878980435431004,0.3134777414315927,0.032674625515937805,0.03650235405124222,0.17769570788368583,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,5.492338321637362,2.6560609340667725,2.570058584213257,0.8652635030448437,0.0875911305973692,0.9637769497931004,0.3656456854604292,0.05650462582707405,0.05429796021188568,0.194145925110206,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,10.19978098361753,2.0195775032043457,1.9071320295333862,0.8179957047104836,0.08465374944501382,0.8209072127938271,0.3152779259985869,0.04270646721124649,0.04111741640261989,0.17915078392252326,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",70,34.343450787477195,1.310463309288025,1.1948068141937256,0.7608897387981415,0.09188172675381653,0.7416024282574654,0.3500645882854528,0.035474181175231934,0.041798442912829754,0.176073607057333,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",120,57.94548936584033,1.200643539428711,1.0375217199325562,0.7424948550760746,0.08905602494044838,0.643036499619484,0.2963496552741562,0.023386627435684204,0.03247280393628466,0.1760745409410447,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",170,82.1509823622182,1.0905241966247559,0.9178407788276672,0.7348929531872272,0.08681057207228701,0.6553110051900148,0.3060014172777329,0.027772121131420135,0.036561795461028515,0.17573321401141584,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",190,91.60742604662664,1.0986202955245972,0.9074190855026245,0.7373322062194347,0.08526927211259426,0.6650092881172895,0.32248710301316863,0.026292651891708374,0.035404791467430065,0.17394885700196028,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,9.453170414315537,2.106332540512085,2.0445005893707275,0.8371907286345959,0.0824134469150507,0.8457772620022297,0.27528457652906924,0.04031410068273544,0.03911874552193183,0.17986190784722567,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",70,33.09139433200471,1.3429651260375977,1.266248345375061,0.776061411947012,0.0693946803573505,0.7047889437526464,0.3214339124423096,0.030615054070949554,0.043568240683133544,0.176646996056661,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",120,57.5025845724158,1.0471056699752808,0.9688077569007874,0.7533871978521347,0.0856358122140236,0.6487891729921103,0.3371188764691501,0.025989603251218796,0.042367392721785836,0.17879362893290818,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",130,62.206075962865725,1.059133768081665,0.9180580377578735,0.7579952590167522,0.10332257441885864,0.6637328006327152,0.337548473105565,0.025477439165115356,0.03879511892676156,0.17281881696544588,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",180,85.76986731262878,0.9451913833618164,0.8165911436080933,0.7561360225081444,0.09843700482856463,0.6581858079880476,0.3502244725855204,0.028452757745981216,0.044010864929614084,0.17486427491530776,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",250,119.74267551884986,0.9721285104751587,0.7523894309997559,0.7532920092344284,0.09198212866420298,0.696257634088397,0.35460434212519515,0.03051656484603882,0.04318361702753652,0.18045670306310058,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,14.158328239573166,1.751932144165039,1.7640329599380493,0.8148471787571907,0.08101305598511878,0.832620408385992,0.3203974734773586,0.04025724530220032,0.04268639735612262,0.1808308579493314,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,18.86326671601273,1.4966100454330444,1.5180631875991821,0.7902152091264725,0.07906372903277244,0.7964129578322172,0.33312076394871526,0.03922542557120323,0.040297558627064856,0.17634604405611753,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",90,43.39244794519618,1.242079734802246,1.1416912078857422,0.7564380690455437,0.08301036405416169,0.7224288173019886,0.29240144185892286,0.03179018199443817,0.036516339453023086,0.19353405898436904,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",140,67.4846165694762,1.1599299907684326,0.9596434831619263,0.7511322200298309,0.07342881982465226,0.6781224198639393,0.3250819951876328,0.02777932584285736,0.03901148381456816,0.1832382739521563,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",160,77.5187611519359,1.1282505989074707,0.9583591222763062,0.7489584200084209,0.09569388716402834,0.714743509888649,0.3654233437149764,0.03482108563184738,0.04631405544017021,0.1799682080745697,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",210,101.93393633817323,1.0627238750457764,0.8011802434921265,0.7445353046059608,0.07368380956510089,0.7032035104930401,0.3181601846520164,0.031170152127742767,0.037059547881507365,0.17861953494139016,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,18.92352516273968,1.4190624952316284,1.394066572189331,0.7795107811689377,0.07895555124864567,0.7233081068843603,0.30629025634128276,0.032888785004615784,0.04186500369359533,0.17601475887931883,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",90,42.46448531351052,1.2342177629470825,1.0568114519119263,0.7586035989224911,0.09211721273519849,0.6863973867148161,0.2846745273917322,0.02883405238389969,0.036253246313773455,0.17182987602427602,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",140,66.01850504870526,1.1447267532348633,0.9215128421783447,0.7567126825451851,0.08715700123091329,0.6830892525613308,0.26465363896231714,0.02684357762336731,0.0304890108756065,0.17713287682272494,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",150,70.74688459280878,1.137915015220642,0.9556535482406616,0.74142637103796,0.0910664440339398,0.664056807756424,0.2866559656288091,0.02592737227678299,0.03270365998070101,0.17668659705668688,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",220,104.01884973910637,1.1075886487960815,0.8562193512916565,0.7431318946182728,0.08863493361456251,0.6079499572515488,0.2581934381878249,0.019319690763950348,0.02589666185263834,0.17909984500147402,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",260,122.69484178465791,1.1476820707321167,0.8411137461662292,0.7620187550783157,0.08173127020825534,0.6230230610817671,0.23834408458000025,0.020742282271385193,0.02310365902681645,0.17039549793116748,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",310,146.05692710797302,1.0287748575210571,0.7913301587104797,0.7316545434296131,0.07957140760369309,0.6062419228255749,0.25607303675163484,0.018854089081287384,0.02327084933856821,0.17391789820976555,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",320,150.7073954383377,0.8677739500999451,0.7061667442321777,0.7378542311489582,0.08137216343487055,0.6176235489547253,0.23412746974833906,0.023089125752449036,0.02467585148866596,0.17572934087365866,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,4.667221260024235,2.629956007003784,2.5698421001434326,0.8707594349980354,0.07167083508620332,0.9021533206105232,0.24297994747307192,0.04584178701043129,0.03603886419238986,0.17404451593756676,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,28.04548563505523,1.3036772012710571,1.2887581586837769,0.7792363129556179,0.09248303711260067,0.7538957856595516,0.36668557379262,0.037284303456544876,0.04768767599366747,0.17704517301172018,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",80,37.32848289213143,1.2234420776367188,1.1475991010665894,0.7611103840172291,0.08407816556059179,0.7406706884503365,0.35442510826578866,0.0372595451772213,0.04505810741196773,0.1743002231232822,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",130,60.64964189589955,0.9373338222503662,0.8264631628990173,0.7529298812150955,0.08679093913471206,0.7218266949057579,0.35097193812899496,0.034508224576711655,0.041605944054635344,0.17438108404166996,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",180,84.01295714173466,0.8680737018585205,0.7469745874404907,0.7506734132766724,0.08971245500272991,0.7365219034254551,0.3596858867702228,0.03961668908596039,0.04564635311009057,0.17853462789207697,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",230,108.43367928662337,0.7634603977203369,0.6516391038894653,0.7513731345534325,0.08623106403784675,0.6483565550297499,0.3715860319725609,0.026576921343803406,0.04257596796247275,0.17453437112271786,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.0031410218216478825,7.299557685852051,7.2821455001831055,0.9770199470221996,0.01195108938636966,2.019215002655983,0.3069971827625414,0.18670821748673916,0.11482436871824415,0.1722464298363775,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,4.490303491940722,2.730313301086426,2.708352565765381,0.8662107847630978,0.086949304485694,0.9437735229730606,0.35622031291810907,0.055186834186315536,0.0508524574300234,0.17034109705127776,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",70,31.757089469349012,1.309952974319458,1.2232215404510498,0.7666631862521172,0.0969295533391134,0.659503573551774,0.33121666355704255,0.025763459503650665,0.03864503186699909,0.17850685515441,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",120,54.55761175160296,1.2335141897201538,1.1219791173934937,0.7536639794707298,0.0949875378903858,0.6786410510540009,0.35318119715988877,0.027492452412843704,0.04320146423836747,0.16936731594614685,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,17.804362368071452,1.5098447799682617,1.48281729221344,0.8011490665376186,0.07835548765083471,0.7804802991449833,0.30756786890494253,0.03700076788663864,0.04137216464585911,0.16999070206657052,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,22.260231512598693,1.4502195119857788,1.4176913499832153,0.7939450815320015,0.08481119390233395,0.7638023868203163,0.2710190656721363,0.03838786482810974,0.03449879705869547,0.17520083114504814,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",100,45.52265985333361,1.2314536571502686,1.0917264223098755,0.7615035213530064,0.0802927470401397,0.6934276930987835,0.28896861183361516,0.033769913017749786,0.04100454126680905,0.16743173194117844,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,12.55322195624467,2.2211077213287354,2.074808120727539,0.8402720727026463,0.09118884586875653,0.8707575798034668,0.27303383148044474,0.048059336841106415,0.044910468236053415,0.4753903750097379,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,38.05269267724361,1.4802736043930054,1.1377708911895752,0.7761548087000847,0.0931142463894961,0.7483074255287647,0.2564060953395017,0.031643807888031006,0.02829902565101109,0.48419457604177296,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",80,101.49442882009316,1.0870635509490967,0.5694859027862549,0.7637309245765209,0.08233654873956618,0.68461474776268,0.21801988777624867,0.025703079998493195,0.021823155187115837,0.4747852840228006,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",130,165.31398838781752,1.0025209188461304,0.3072475790977478,0.7571765594184399,0.07495344850165629,0.6747713945806026,0.20687383555232655,0.026549920439720154,0.021892287811057617,0.4737964450614527,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,25.862675296957605,1.5778647661209106,1.390820860862732,0.8081652969121933,0.07725599472721172,0.7552798055112362,0.19451111602407806,0.031054489314556122,0.02372911433617512,0.4793893330497667,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",70,90.06388228491414,1.0939013957977295,0.5847060084342957,0.7587285377085209,0.07750125207913083,0.6985839828848839,0.23210604041968655,0.02774903178215027,0.024984824577463396,0.48023169196676463,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",100,128.4653510331409,1.0615791082382202,0.5101629495620728,0.7629988342523575,0.07288170737702535,0.6968371402472258,0.2908694835637266,0.028574593365192413,0.031180590532500625,0.4764871801016852,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,12.567929615848698,2.1342453956604004,1.9882721900939941,0.8260494768619537,0.0803028689374224,0.8726441822946072,0.30040594521765096,0.04792679101228714,0.043584472114761214,0.46820301201660186,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,76.330370099633,1.0688722133636475,0.5831626057624817,0.7597688399255276,0.07771194163588262,0.6577452775090933,0.16938385197475955,0.023623697459697723,0.01849232883776055,0.47996628494001925,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",110,139.67261650343426,1.1226000785827637,0.36116325855255127,0.7569855861365795,0.07201846990442172,0.6242723260074854,0.15275874939496295,0.01904091238975525,0.014724091541170735,0.4801801950670779,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.0064435910899192095,7.905442237854004,7.766335487365723,0.97909364849329,0.012710456405869647,2.0391848161816597,0.3652276287209317,0.2127812523394823,0.12068849867673358,0.4713781450409442,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,63.07983455853537,1.175406813621521,0.693218469619751,0.7635825015604496,0.06265015710771862,0.6633815765380859,0.20809672029137563,0.024019718170166016,0.020532784458742827,0.4808619061950594,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",120,153.47602452337742,0.8336263298988342,0.1978437900543213,0.7398671656847,0.06128070781832181,0.5711748860776424,0.1534286135817514,0.016071900725364685,0.014170229222288732,0.47961244313046336,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.006044603884220123,7.4197998046875,7.579885959625244,0.9773546494543552,0.012325672119587641,2.0398045405745506,0.35455843723148045,0.19882270507514477,0.12463197274705928,0.48293051309883595,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,12.540541884955019,2.1364169120788574,1.930395483970642,0.8410623669624329,0.0744138891654061,0.8210418149828911,0.2352237277845438,0.03933638334274292,0.035037314479254095,0.46619324409402907,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,77.97720711585134,0.8928890824317932,0.5522797703742981,0.7722126431763172,0.07406668665568021,0.7010830417275429,0.21122384388477483,0.030078992247581482,0.025557990130326638,0.47769304108805954,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",110,141.1956486343406,0.8058848977088928,0.29266810417175293,0.7631473913788795,0.06796438608113783,0.6520735286176205,0.1683394736216607,0.023675382137298584,0.018219627444325013,0.4689090910833329,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,12.780948357190937,2.0733726024627686,2.0393710136413574,0.8344424217939377,0.08329106635760274,0.8359222672879696,0.2805243649584157,0.04311230778694153,0.040561197128763854,0.4779351579491049,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,38.33809373551048,1.3321504592895508,1.0412793159484863,0.7826833017170429,0.0880996400244215,0.7448879182338715,0.25416582461511533,0.0336996391415596,0.030050850383370072,0.4553251869510859,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",80,101.64072061819024,1.0342810153961182,0.4697064757347107,0.7460262961685658,0.060200417395905675,0.6699766013771296,0.2065276543726788,0.025395743548870087,0.023894024424006552,0.4533684179186821,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",130,167.2073026052676,0.957991361618042,0.27795109152793884,0.7378810569643974,0.06761743264543595,0.667441587895155,0.24884689547857552,0.027447037398815155,0.02878232307770393,0.45836536795832217,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.0065208610612899065,7.460413932800293,7.416133880615234,0.9742115661501884,0.013312537700882494,1.9248125776648521,0.34828258139259183,0.1752600073814392,0.11907691816158743,0.45505055389367044,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,12.552531003719196,2.224038600921631,2.1222879886627197,0.8391457386314869,0.07904993202512144,0.8505270667374134,0.21554274896385317,0.04114670306444168,0.032463569178860514,0.45955951209180057,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,76.34993166918866,1.167109727859497,0.6912184357643127,0.7507650256156921,0.09078398566197174,0.682098900899291,0.21566145581309232,0.027137845754623413,0.025979402147655213,0.45253790100105107,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",120,152.40388509002514,1.1447402238845825,0.4288684129714966,0.7538459412753582,0.0845961578321056,0.6392990406602621,0.20249776923933846,0.023247234523296356,0.021087847294098554,0.448656806955114,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",170,215.38109340472147,0.9487118721008301,0.24606837332248688,0.7682819478213787,0.06381004154530107,0.670026833191514,0.20878062850589624,0.02686188369989395,0.025724659826731874,0.455467171035707,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",180,227.9113110967446,0.9873702526092529,0.22143693268299103,0.7666033320128918,0.076481551722335,0.6240553855895996,0.17298807782620731,0.020916588604450226,0.018606612367272374,0.46157468506135046,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,50.092615742236376,1.187846302986145,0.9027127027511597,0.7714485004544258,0.07838990112207168,0.7176487557590008,0.20142967644604393,0.03193492442369461,0.029053250847350505,0.4593647699803114,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",90,113.69748034258373,0.8521472215652466,0.33442285656929016,0.7509454227983952,0.07221863325286408,0.5765091963112354,0.08272842164988528,0.016391515731811523,0.01340464863646227,0.46152510889805853,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",140,180.55144400382414,0.7527387142181396,0.18953412771224976,0.729823712259531,0.06581580133337948,0.5648317188024521,0.10289185849471975,0.014150187373161316,0.009506849429746679,0.45282661193050444,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",160,207.6375718335621,0.7688517570495605,0.11180582642555237,0.7385270446538925,0.061931374169278436,0.6001472137868404,0.1437451057423428,0.021440468728542328,0.0211121330449727,0.4501391800586134,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,51.353915938176215,1.2386844158172607,1.0455787181854248,0.7671193182468414,0.08492293579451685,0.7146374993026257,0.22735614238741125,0.030017174780368805,0.026858959644874638,0.46064995182678103,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",90,115.12767218076624,0.9844810962677002,0.4672577977180481,0.757657878100872,0.06346631888685629,0.6779462210834026,0.21979231819239756,0.027218468487262726,0.02591400742450103,0.45404348615556955,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",140,182.10627966490574,0.9426374435424805,0.2240748256444931,0.7613478787243366,0.06955093724404661,0.6399432178586721,0.18527358470991465,0.02239571511745453,0.01843286871462067,0.45046369615010917,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",150,195.0324047463946,0.9375015497207642,0.19532160460948944,0.7436808943748474,0.06023281497637671,0.6613896209746599,0.17562851951670652,0.021851547062397003,0.017180676146083346,0.47851963294669986,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,28.162492273375392,1.6379380226135254,1.4409185647964478,0.8028914220631123,0.08559124517873179,0.7828352767974138,0.31700468541602006,0.04200832173228264,0.043032980593316034,0.4631709000095725,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,83.979747509351,1.2035237550735474,0.7282818555831909,0.767608679831028,0.08843164460915147,0.7274958044290543,0.29171339912290345,0.0345858633518219,0.032907642616726886,0.4593601010274142,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",110,147.9719278323464,0.9345957636833191,0.3401881456375122,0.767150029540062,0.08879398600023077,0.6769253946840763,0.21836994187558112,0.02777620404958725,0.02524074854493423,0.45608433918096125,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",120,160.9126886180602,1.0172309875488281,0.35089221596717834,0.7591575942933559,0.06558679797919274,0.6707272790372372,0.21457164891903083,0.028976328670978546,0.029477029666893088,0.4668762928340584,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,42.89314603013918,1.6989531517028809,1.2287025451660156,0.8065483868122101,0.07434735034167664,0.7422235645353794,0.13866514207007388,0.03378625959157944,0.027596922175842604,0.9179684200789779,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",70,149.88171569944825,1.3135297298431396,0.4535013437271118,0.7550627663731575,0.07258932186235469,0.6228970400989056,0.1717847214928007,0.01899910718202591,0.013683812398881624,0.8865591789362952,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,21.689993297914043,1.9948956966400146,1.8526545763015747,0.8360873535275459,0.0744649668466739,0.8642360009253025,0.2703290673683127,0.044152453541755676,0.034218718058183765,0.9243766330182552,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,127.21894562686794,1.3104901313781738,0.5527122020721436,0.7745042890310287,0.049037453819705916,0.6928686294704676,0.1456343980397463,0.026986561715602875,0.021853120936051216,0.9102472969098017,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,62.93991536879912,1.2127469778060913,0.8373415470123291,0.7963802963495255,0.0793633725786332,0.7035244777798653,0.19402577951769445,0.028414271771907806,0.029174274700631133,0.8909542789915577,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",80,170.74924069282133,0.8255857825279236,0.15262874960899353,0.7629738599061966,0.07409140999840441,0.6631473749876022,0.1598685734020535,0.026226602494716644,0.029065530317693815,0.8739710249938071,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,21.001583840930834,2.0681328773498535,1.8892550468444824,0.8464637845754623,0.0669602502520627,0.8272415064275265,0.18663457910541315,0.03858514875173569,0.029970879190053853,0.9105259960051626,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,44.97594367119018,1.536408543586731,1.2703884840011597,0.8250986263155937,0.08330572377147405,0.7818713076412678,0.23851522184488236,0.035698533058166504,0.03316957449790443,0.8728313529863954,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",80,174.50417295226362,1.3036506175994873,0.19706037640571594,0.7723709605634212,0.062108673520682244,0.6616922114044428,0.1954595238323211,0.025405816733837128,0.025884084856093115,0.8760658828541636,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,43.72367281187326,1.6116611957550049,1.3125852346420288,0.8125323466956615,0.09037826125090863,0.7547050192952156,0.2096771855594928,0.0300259068608284,0.02431048671123365,0.89209815999493,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",70,149.72004196280614,1.3432378768920898,0.3795410394668579,0.771300908178091,0.06216070596479243,0.6274111550301313,0.13855436682161953,0.021046072244644165,0.018042589962682702,0.8660822720266879,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",80,171.11745982104912,1.3339868783950806,0.31097447872161865,0.7803033925592899,0.06430590959882736,0.635858703404665,0.17687386162933458,0.025016412138938904,0.02412487954505795,0.9203067030757666,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,85.15999246900901,1.338310718536377,0.6965028047561646,0.7734386324882507,0.08462844961782064,0.6909698750823736,0.16515075246860153,0.027206487953662872,0.018309557963678492,0.9035288298036903,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",90,191.9741213528905,1.3262630701065063,0.21543389558792114,0.7566841393709183,0.07089186861402523,0.6136756371706724,0.20753835188874528,0.019323736429214478,0.02029673504471424,0.8755538808181882,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,108.78965988149866,1.0755213499069214,0.43502816557884216,0.7739536501467228,0.0798947178100998,0.6654152274131775,0.2148512879316338,0.026703253388404846,0.023760413491502364,0.8778491618577391,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,129.80837027612142,1.2367899417877197,0.41952601075172424,0.7847597487270832,0.07815455371461046,0.6765371561050415,0.24503894366832296,0.02701772004365921,0.02554953291101308,0.9079404729418457,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,42.17059112060815,1.6248698234558105,1.158167839050293,0.7892120145261288,0.06641485232328805,0.7617520578205585,0.23063553483940563,0.0349966362118721,0.032242432217095086,0.8958579751197249,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",70,149.62344167544506,1.2371176481246948,0.28937894105911255,0.7650685049593449,0.06395687991021613,0.6716491747647524,0.27726814477776995,0.02629902958869934,0.031290986405879684,0.8655376899987459,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",120,258.1094138927292,1.5059614181518555,0.15441592037677765,0.7722176313400269,0.07216134579682562,0.6661400925368071,0.2387376674252969,0.02694590389728546,0.030282523120302208,0.9068367639556527,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.009475596947595477,7.980016231536865,7.88364315032959,0.9793992005288601,0.013533740312237981,2.1290415078401566,0.3302516320704973,0.22773073613643646,0.11087852755169571,0.8902319199405611,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,130.9222114519216,1.242746114730835,0.5461927056312561,0.7831433787941933,0.062097300972758014,0.7147784251719713,0.22125224534733173,0.029337361454963684,0.024362827158669716,0.8837025901302695,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.009551340015605092,7.779542922973633,7.796748161315918,0.9831532984972,0.006904877091921214,2.2014075592160225,0.42927137745780175,0.20690125040709972,0.12487600456986607,0.8864606129936874,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,108.27450620522723,1.1880637407302856,0.5579319000244141,0.7863429524004459,0.05517069380509082,0.704673120751977,0.1884354090652478,0.02956300973892212,0.022223461831788194,0.8929280829615891,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,129.34286872879602,1.265939712524414,0.5116915106773376,0.7704471796751022,0.055243918412002616,0.6605794969946146,0.15457698612006499,0.024025700986385345,0.01577791302036337,0.901845530141145,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,1.0374842260498554,6.410079479217529,6.448113441467285,0.9740589074790478,0.014402502613853067,1.7926518991589546,0.27572989385542657,0.1344515811651945,0.0870451845812175,0.07678839098662138,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",70,33.21291995001957,4.804299354553223,4.721943378448486,0.9501664452254772,0.03282927944593573,1.3707682900130749,0.3736774545210081,0.08722921833395958,0.07620088301231781,0.07780258101411164,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",110,51.487523728981614,4.7752156257629395,4.555578708648682,0.9454968236386776,0.04024204257929736,1.396241519600153,0.3266898726645992,0.09121216461062431,0.07538839045977902,0.07450143399182707,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",120,56.030206241994165,4.677412986755371,4.569985866546631,0.9433895908296108,0.04386987947461779,1.3577579222619534,0.36790198198026075,0.0893910862505436,0.08415921266531134,0.07317009102553129,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",170,79.0270551709691,4.756505966186523,4.500253677368164,0.9414347894489765,0.044923310737384155,1.3951605297625065,0.3398556237242671,0.09414884075522423,0.07377166365621392,0.11411647603381425,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,1.1598489510361105,6.956441879272461,6.932518482208252,0.9748830460011959,0.013383985175096834,1.9224481582641602,0.23963223132880385,0.1461369562894106,0.09576742714894759,0.07731961901299655,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,23.829233631026,4.880883693695068,4.78165864944458,0.9411003962159157,0.04521070489995875,1.3793475218117237,0.3378136887161408,0.09279290586709976,0.0829959366799493,0.07453912403434515,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",70,33.12116958689876,4.856291770935059,4.643151760101318,0.9404285438358784,0.04724378914059357,1.3749816492199898,0.38344001585020543,0.0873318500816822,0.08020504074600682,0.07475172297563404,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,1.0263224419904873,6.195229530334473,6.25598669052124,0.9735413603484631,0.012779769773952326,1.6900361478328705,0.3171897285539849,0.12350819632411003,0.09833813753127812,0.07565675000660121,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,23.850163116003387,5.009012222290039,4.804600238800049,0.957367829978466,0.025899777623436843,1.384970087558031,0.36772164438805865,0.09210449457168579,0.07800096384170176,0.07404626195784658,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",100,46.661729109007865,4.729039192199707,4.680451393127441,0.9437883123755455,0.04572879910033387,1.362984262406826,0.341880036811447,0.089219119399786,0.07932763587846398,0.07559055788442492,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",110,51.24742590205278,4.7251715660095215,4.531838417053223,0.9377146027982235,0.04899619666112955,1.3447706289589405,0.3716869814544471,0.08321257308125496,0.07788118940974438,0.07666537899058312,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",160,74.06029999593738,4.716724395751953,4.50202751159668,0.9445506632328033,0.0470621624255447,1.3567088209092617,0.36704291707182257,0.08622609451413155,0.07966403052067951,0.07433545391540974,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,14.717271031928249,5.103413105010986,4.89590311050415,0.9546197578310966,0.025669445963942803,1.4363607317209244,0.32972393928864097,0.09953828155994415,0.07890287699204727,0.07467567001003772,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",80,37.59332505916245,4.726034641265869,4.59366512298584,0.9379284679889679,0.05526358099312193,1.3643395006656647,0.35093908977610705,0.08420676738023758,0.07031194618963428,0.0738908511120826,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",90,42.23980830493383,4.793632984161377,4.637890815734863,0.9456804245710373,0.045022314503313274,1.3345299325883389,0.3427587051142391,0.08340828120708466,0.07906976725879414,0.07265390199609101,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,1.148740183096379,6.291143417358398,6.353158950805664,0.9739363603293896,0.013265948250431288,1.7177017778158188,0.2749915365837449,0.11829552799463272,0.08676824385031512,0.07510066591203213,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,23.844164356123656,4.945468902587891,4.796372413635254,0.9457100182771683,0.03768479606566453,1.3743584752082825,0.3485657491149741,0.0916479118168354,0.07871786518566194,0.074132981011644,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,1.0183739860076457,6.240069389343262,6.31292724609375,0.9723211005330086,0.013660963816639893,1.6849333345890045,0.2949177100501965,0.12012692540884018,0.08776775472090968,0.07489633397199214,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,10.165043160086498,5.1253557205200195,5.051243305206299,0.9628587700426579,0.014633384127866148,1.3752447217702866,0.3123426228215726,0.08871477842330933,0.07669792589570203,0.07481562299653888,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",70,33.62227198109031,4.956015586853027,4.7560954093933105,0.955413706600666,0.02351866984459116,1.3735835626721382,0.30852950136041346,0.08959497511386871,0.07410927866710652,0.07417795318178833,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,1.018734676996246,6.271370887756348,6.346018314361572,0.9733873568475246,0.013150968239859987,1.7110945656895638,0.2731054509924661,0.11847201362252235,0.08545747379251467,0.07334923394955695,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,23.811784677905962,4.911929607391357,4.763812065124512,0.9528193399310112,0.03091709655165238,1.384882178157568,0.32784813522699596,0.08985737711191177,0.06754676932496806,0.07608881685882807,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",60,28.369372508954257,4.886588096618652,4.679598808288574,0.9470220319926739,0.04426857871570278,1.4099354296922684,0.36485169334276274,0.09359246119856834,0.07805160715426977,0.07493193401023746,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",110,51.09105447214097,4.724736213684082,4.562694072723389,0.9471427164971828,0.0356607566008573,1.3733273297548294,0.38019311147991086,0.08913085609674454,0.08424058239703007,0.07454419299028814,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,14.774717531865463,5.065130233764648,4.9828691482543945,0.9582803882658482,0.02012457143447485,1.403555005788803,0.357568463902356,0.09624838456511497,0.09073651458714538,0.07526497798971832,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",80,37.253186187008396,4.781054496765137,4.678412914276123,0.942658182233572,0.04296702000726841,1.3342714123427868,0.32647261119605886,0.08421501889824867,0.07052397382131487,0.07509840792044997,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",90,41.95546910702251,4.830554485321045,4.603180885314941,0.9469592720270157,0.04166787071384388,1.3735216744244099,0.37141184145254513,0.08604274317622185,0.07619125804158437,0.07528482284396887,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",140,65.2429955850821,4.633084774017334,4.5359930992126465,0.9452500194311142,0.04294500789222703,1.3584083765745163,0.37557532978739383,0.08874476701021194,0.08354345583113122,0.07593102706596255,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,5.638860469916835,6.856346607208252,6.864893913269043,0.9761641323566437,0.011506534459701014,1.9275293499231339,0.2855561339774162,0.1507427953183651,0.09690560652921777,0.07569131697528064,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,14.742741874884814,4.980635166168213,4.899865627288818,0.9526990987360477,0.027574427049314554,1.3609259501099586,0.33909996865135195,0.09057844430208206,0.07795371171600436,0.07530205394141376,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",80,37.413600211031735,4.8870391845703125,4.655568599700928,0.9462877884507179,0.0375080389454396,1.3382895402610302,0.3298191262439193,0.08377950266003609,0.07569604867262585,0.07572326087392867,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,1.0128354381304234,6.339508533477783,6.391435146331787,0.9726557247340679,0.014246420621441297,1.7061612010002136,0.22873005966145427,0.12157249823212624,0.07803916964271448,0.07422588299959898,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,23.672116908011958,4.866432189941406,4.737885475158691,0.9437858536839485,0.04529133556874055,1.3588490523397923,0.3519295223568194,0.08770923689007759,0.0788781618595067,0.07519376114942133,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",100,46.26180940004997,4.732131004333496,4.559374809265137,0.9399728700518608,0.04777961512559965,1.3410566933453083,0.38226167524801163,0.0823124386370182,0.07750137552710261,0.07625671499408782,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",110,50.82579600205645,4.7785186767578125,4.560297012329102,0.9395745135843754,0.045018947857106875,1.339869987219572,0.3447663631648258,0.08151888847351074,0.07063569954275213,0.07481428887695074,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,36.67305660794955,2.8104746341705322,2.294052839279175,0.8628221675753593,0.046943315295711434,0.9260192438960075,0.3297444806112376,0.04935653507709503,0.043609459254129214,0.23675006302073598,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",90,80.03100306598935,2.098090887069702,1.7186037302017212,0.8206811659038067,0.0714844172895923,0.7847107592970133,0.271182160675332,0.03238900005817413,0.03274630053122748,0.24603726400528103,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,1.8536497669992968,5.759296417236328,5.815774917602539,0.9669510684907436,0.01909129594262073,1.487025536596775,0.3536008411654119,0.08584407344460487,0.06713753391383344,0.22584810701664537,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,10.687293234979734,3.2495064735412598,3.1955909729003906,0.899751465767622,0.022343853276732047,1.0184786207973957,0.32057229500011947,0.04897081106901169,0.041057351574903506,0.22403764096088707,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",60,54.01597079006024,2.383756399154663,2.0175740718841553,0.8286155201494694,0.059145187276077954,0.7929356135427952,0.2834531209202948,0.03307581692934036,0.035564849901917356,0.2267777780070901,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,19.20118358102627,2.876434326171875,2.7686679363250732,0.8740546517074108,0.05384749856500779,0.904301680624485,0.2722759493676062,0.04378261789679527,0.04720820534583745,0.22477295296266675,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",80,71.11003627593163,2.491889476776123,1.9725630283355713,0.8256259299814701,0.07334087249048493,0.7433894034475088,0.2557756036093555,0.02907227724790573,0.029394097662104237,0.22876202897168696,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",90,79.76663399802055,2.0329384803771973,1.5946658849716187,0.8250422663986683,0.04569439708778513,0.7892317809164524,0.24880448691652937,0.03006681799888611,0.028794971766386207,0.2243920110631734,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,10.62109127396252,3.6619951725006104,3.602055072784424,0.9281907081604004,0.036028072904510766,1.0445342995226383,0.25596952896144914,0.04426043480634689,0.04003173643117366,0.22610690898727626,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",60,53.737631540047005,2.154928684234619,2.033519983291626,0.8396020978689194,0.05775933247317698,0.862663246691227,0.31549992423839834,0.038664624094963074,0.036918258325396774,0.22436006099451333,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",110,97.20118598593399,2.144218921661377,1.6389374732971191,0.8011785969138145,0.08464117479044467,0.7305744476616383,0.24248996631520014,0.025934897363185883,0.02564450147995701,0.22982481797225773,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",120,105.77931067289319,2.0105395317077637,1.5448276996612549,0.8213823810219765,0.08098962908891766,0.8075040131807327,0.25489898820854895,0.033545397222042084,0.031003979569445728,0.22725496301427484,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,19.28779148310423,2.6623499393463135,2.596465826034546,0.8677975758910179,0.051247438931121055,0.942169152200222,0.29090128627672596,0.04889042675495148,0.044014189309622975,0.2306131690274924,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",70,62.36984572908841,2.154633045196533,1.8323049545288086,0.8078305125236511,0.07499959717333933,0.7561075538396835,0.22544097798260251,0.028476811945438385,0.025608339373587093,0.23003914905712008,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,10.503607901977375,3.45284366607666,3.453158378601074,0.9122903905808926,0.026762326419108563,0.99666927754879,0.24693529880721235,0.04922214150428772,0.03171328013350206,0.22948976093903184,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,19.27659401902929,2.783254384994507,2.682178020477295,0.8641049787402153,0.042866583664498754,0.9193730279803276,0.31650038808949427,0.04309995472431183,0.040249782502992826,0.22747284802608192,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",70,62.58512226282619,2.2559752464294434,1.8847781419754028,0.830473717302084,0.07850606730648971,0.7603355329483747,0.23802667943032238,0.028444863855838776,0.025831589272048122,0.22928457311354578,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",120,105.64628727198578,2.4902966022491455,1.5776491165161133,0.829804178327322,0.06320731019657407,0.8446922395378351,0.3344849468313362,0.04232179373502731,0.041891355818003954,0.2273867439944297,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,36.39086723304354,2.7681469917297363,2.4909474849700928,0.8528752438724041,0.07158535158511559,0.8036926835775375,0.26114431694203905,0.030565358698368073,0.02991874995237315,0.22863182402215898,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,45.18510295194574,2.693080425262451,2.175072431564331,0.8513748571276665,0.06464778826520509,0.7594504468142986,0.19284586469258413,0.02601531147956848,0.0173256692659164,0.2296515740454197,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",100,88.22639707406051,2.5387134552001953,1.9113761186599731,0.8393872603774071,0.051773753282113165,0.7853303607553244,0.267979231125061,0.024593889713287354,0.025484483758722844,0.2257591960951686,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,27.8884707079269,2.6604058742523193,2.4401891231536865,0.8544017970561981,0.048922420888065664,0.8352866545319557,0.30610425987197604,0.038710422813892365,0.04123273726872844,0.22305667190812528,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",80,70.93521329201758,2.0709972381591797,1.6321545839309692,0.8092036657035351,0.07042699656971603,0.7886674255132675,0.23015038443939223,0.0338289812207222,0.028783732219489544,0.2219714850652963,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",100,88.30247680307366,2.738645315170288,1.6916359663009644,0.820006862282753,0.04623269136244539,0.827694121748209,0.25070102711694425,0.03544125705957413,0.026074958410925667,0.2230952470563352,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,27.794023535912856,2.542736530303955,2.503335952758789,0.855681162327528,0.05341384998784116,0.8962160684168339,0.3582545496930117,0.046130649745464325,0.05377016238646041,0.22235683514736593,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,1.8519432661123574,5.7850847244262695,5.864657402038574,0.9686586931347847,0.018497253735632095,1.512563094496727,0.3462543529709156,0.08894122019410133,0.068082256307422,0.22245780006051064,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,45.137623575981706,2.4694721698760986,2.271199941635132,0.8471038080751896,0.07298286629473644,0.89351736754179,0.2757539887439525,0.047783974558115005,0.04246045582167053,0.22054060106165707,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",60,53.94714098284021,2.393636703491211,2.046812057495117,0.8293147310614586,0.06335135022585817,0.7837048955261707,0.26959110557062804,0.030985787510871887,0.03200508324599547,0.22120355698280036,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",110,97.21159828896634,1.949477195739746,1.4616782665252686,0.8061842508614063,0.06041954567482109,0.789975356310606,0.28943458771709524,0.03585962951183319,0.03409848117639058,0.22155949589796364,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,16.236779795959592,3.0058555603027344,2.9981815814971924,0.8975235596299171,0.0513735342228741,1.0507596172392368,0.26186902589292976,0.049820832908153534,0.03482425482535395,0.4254637890262529,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",60,82.083007848938,2.398176908493042,1.7436168193817139,0.8319076895713806,0.0608349776825613,0.8168311454355717,0.22157591973727436,0.03516297787427902,0.027737492818801585,0.39490940293762833,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,2.888054718961939,6.435772895812988,6.548543930053711,0.9730818830430508,0.014405448504430264,1.6981685310602188,0.2800836437622596,0.11191707849502563,0.08190835460147812,0.39427022298332304,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,68.09222837002017,1.7888941764831543,1.3823696374893188,0.789459191262722,0.0694102216477775,0.7273012511432171,0.21919956541685537,0.028202727437019348,0.02572687980455911,0.39364926295820624,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,2.8511716569773853,5.748318195343018,5.828367233276367,0.9671943821012974,0.01515611468277662,1.4804884940385818,0.3382136433328555,0.08330732956528664,0.06444169937886807,0.3935350279789418,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,68.20009694504552,2.289782762527466,1.6763852834701538,0.8253201581537724,0.05847868894561589,0.8093075342476368,0.2803643271146213,0.034238703548908234,0.033939889803115256,0.393085437011905,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",60,81.29850763303693,2.1988894939422607,1.8430149555206299,0.8425591886043549,0.05269308540433976,0.8632414899766445,0.2741654326976764,0.05138377845287323,0.060751796806053825,0.38717724895104766,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,69.06395793298725,1.7845990657806396,1.4445862770080566,0.7859844788908958,0.07073391180446595,0.6776327677071095,0.23719862816588996,0.02157493680715561,0.02168288736089218,0.39024376799352467,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,15.9009777570609,2.9659876823425293,2.9640562534332275,0.8905335776507854,0.04073602099139776,0.9272559843957424,0.19864021011534852,0.039253976196050644,0.03237108296369696,0.39311491209082305,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",60,81.40412394492887,2.021561622619629,1.6093322038650513,0.8033453784883022,0.07064027387096294,0.7786786705255508,0.24257693103483657,0.03386470675468445,0.03098209792118212,0.397622040938586,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",70,94.88184180296957,1.8092470169067383,1.2679136991500854,0.7763683646917343,0.07066307268230841,0.6658616196364164,0.2811215500436705,0.024052254855632782,0.02561360329798627,0.40401668404228985,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",120,159.37473970791325,1.8251960277557373,0.8148713111877441,0.792052373290062,0.06850734035741846,0.6976530775427818,0.21880640612054442,0.027284860610961914,0.026130741356389882,0.40769521007314324,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,42.42220452497713,2.3219738006591797,2.0871458053588867,0.8449405878782272,0.06712273271501511,0.8261890523135662,0.2770588202812517,0.033991217613220215,0.03526602153448652,0.4088554768823087,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",80,107.03839539899491,2.1214306354522705,1.3000489473342896,0.7965696379542351,0.07521800361404317,0.7229813747107983,0.2861089677332428,0.02579532563686371,0.025472361509427142,0.4061260710004717,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",90,120.47969372896478,1.762927770614624,1.3663840293884277,0.7713939324021339,0.07336219046651223,0.6624884065240622,0.2573749031504752,0.023587949573993683,0.026309283684649118,0.40554927196353674,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,2.772437779000029,5.637829303741455,5.692262649536133,0.9660053476691246,0.01852155327906984,1.429831750690937,0.30612061327718365,0.07881931215524673,0.05625602317082641,0.3847824539989233,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,66.0536727400031,2.223203420639038,1.7228409051895142,0.8330340906977654,0.05854254738889675,0.8456276245415211,0.2975821156624289,0.03303258866071701,0.029731336448106646,0.3848702209070325,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",100,129.28805179987103,2.2263643741607666,1.1136330366134644,0.788238413631916,0.07382716267589476,0.6682337932288647,0.3114805519930374,0.023554429411888123,0.03295008160790132,0.3843336079735309,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",110,141.8712359420024,1.9664688110351562,1.1010072231292725,0.8110950291156769,0.07501912154219352,0.7615339662879705,0.39278327891751336,0.034839797765016556,0.05283073641790574,0.38702906505204737,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,2.6335533522069454,5.767912864685059,5.846485137939453,0.9691160060465336,0.016368318570616425,1.4657260440289974,0.3292348815477159,0.08763044327497482,0.0674567985892553,0.3832574989646673,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,65.85372667619959,2.232393503189087,1.6599400043487549,0.8274236619472504,0.05963894306768784,0.8036503382027149,0.2634145256665489,0.034483104944229126,0.02966853365753334,0.3874582420103252,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,28.023923786124215,2.935648202896118,2.7222232818603516,0.8796103708446026,0.04879570776726557,1.006634596735239,0.3576925718670245,0.045786015689373016,0.041159416243195315,0.39389104302972555,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,40.77980643394403,2.2501885890960693,1.8882639408111572,0.8203762210905552,0.07411763217243657,0.7844304144382477,0.19602312478615133,0.03500550985336304,0.031785087442884004,0.38696547714062035,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",90,116.72752364189364,1.6756095886230469,1.05398428440094,0.7911062799394131,0.08156712524423583,0.7530053537338972,0.30471554264290884,0.03341687470674515,0.03423162044310317,0.38662420492619276,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,28.04634275683202,2.456240177154541,2.328834056854248,0.8418268784880638,0.05833702194406799,0.8444150313735008,0.25558327203849557,0.037214718759059906,0.02930482920344244,0.3691027720924467,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",70,90.85603867890313,1.773240089416504,1.2719374895095825,0.7904038727283478,0.07390303623795547,0.6908713337033987,0.18605087875700865,0.02931932359933853,0.0389927899159323,0.3738744738511741,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",80,103.87396520911716,1.7411562204360962,1.2458503246307373,0.8110274896025658,0.05987368655033264,0.7959765512496233,0.259291133626826,0.04009045660495758,0.03605025310489634,0.37488104309886694,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",130,166.22490161005408,2.1435070037841797,1.0900670289993286,0.7879641801118851,0.07058962849552466,0.6678587198257446,0.17173226613994838,0.021824173629283905,0.01587699512091604,0.3688381100073457,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,26.45982740796171,2.7818033695220947,2.6380348205566406,0.8775948919355869,0.07945430551628158,1.1824526824057102,0.32816851295247684,0.09214393422007561,0.075875800798498,0.3482012009480968,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",90,74.73993469495326,2.438065528869629,2.354776620864868,0.8491540141403675,0.09799950276088583,1.1459049955010414,0.3743759391377645,0.08807264640927315,0.07081511087961913,0.3467785030370578,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",100,81.85101766104344,2.3418498039245605,2.175344944000244,0.8389565534889698,0.09920649011931697,1.1398202255368233,0.3658628521398325,0.08811601996421814,0.07063632573815723,0.3743980119470507,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,33.82224255800247,2.6989474296569824,2.4940454959869385,0.8580645769834518,0.08250982576168558,1.1921733729541302,0.38467007905859163,0.09881085529923439,0.08120541023164708,0.3670633310684934,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",90,72.92330886097625,2.3170523643493652,2.0687665939331055,0.8529098369181156,0.09188908811490269,1.1686461679637432,0.40765429596641,0.09303105622529984,0.07640009445609082,0.30992475198581815,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",140,112.09206864587031,2.334169626235962,2.0972530841827393,0.8450307846069336,0.0930241756762062,1.146296814084053,0.3862122842806795,0.08975274860858917,0.07014364141854997,0.3138247032184154,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",150,122.78174489317462,2.2344555854797363,1.967182993888855,0.834444060921669,0.10658791361241969,1.1467643845826387,0.4143992407234738,0.09659089893102646,0.08011199860007345,0.31266127713024616,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,33.12820000201464,2.509861707687378,2.3709263801574707,0.8479925021529198,0.09910549066196436,1.1442238427698612,0.36941610486943205,0.08909909054636955,0.07300906974181448,0.31016862485557795,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",100,85.89004542701878,2.180913209915161,2.0221402645111084,0.841097716242075,0.10371670954064356,1.1574700139462948,0.41499070164465585,0.09082914516329765,0.07443191078952782,0.3052687749732286,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,9.122397255850956,3.583266258239746,3.3990092277526855,0.9041441045701504,0.057177385912920785,1.2507431842386723,0.33047873414460943,0.10279842466115952,0.07957040158342667,0.3041588820051402,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,17.023754706140608,3.099027633666992,2.974648952484131,0.8900131210684776,0.07368748202395618,1.1958286091685295,0.32382937461835426,0.09477154538035393,0.07551628975735568,0.3054832681082189,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",70,54.90991256805137,2.3969621658325195,2.1547741889953613,0.839322779327631,0.09112595909849583,1.1589898727834225,0.3805176919419772,0.09316903725266457,0.07901950815305303,0.3103740490041673,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",120,92.7095667840913,2.2638819217681885,1.9872348308563232,0.8406052775681019,0.10490039542559676,1.1003734543919563,0.39760708584738275,0.0832633525133133,0.074587549605508,0.3244116969872266,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",180,142.07596978219226,2.0344390869140625,1.7459516525268555,0.8257445283234119,0.10770925092751045,1.1570527851581573,0.451620132027692,0.09151080623269081,0.08427272223227203,0.31224445695988834,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",190,149.39061368792318,2.298264980316162,1.9800283908843994,0.8373392857611179,0.10090329845078347,1.1544800996780396,0.4057674082732291,0.09322445467114449,0.07648075404103973,0.3748423501383513,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,33.12446852307767,2.614788055419922,2.481839656829834,0.851809062063694,0.09329722876411671,1.1349664740264416,0.39338975395680453,0.09038769081234932,0.07620222488071135,0.31099530891515315,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",90,70.61088495096192,2.293588161468506,2.118961811065674,0.8279421031475067,0.11018498613722129,1.1378373876214027,0.3918552928782927,0.09249858930706978,0.0779243073604765,0.31060701911337674,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,2.0300539419986308,5.153534412384033,5.1960859298706055,0.9501049593091011,0.036588441911834475,1.52735997736454,0.2799363474215456,0.12115972489118576,0.08421619463924297,0.6103898279834539,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,17.706315976101905,2.91525936126709,2.878175735473633,0.8864715434610844,0.07915673201077586,1.2039056904613972,0.33589820012356747,0.09080047905445099,0.06856516283988055,0.3657139700371772,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",70,57.96232549706474,2.3221068382263184,2.1135752201080322,0.8485739342868328,0.10711820967887133,1.1256659664213657,0.35102898810230293,0.08378699794411659,0.06519305328878347,0.36687050200998783,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",120,98.03584898100235,2.3532369136810303,2.045189619064331,0.832429114729166,0.10233367314611762,1.143386386334896,0.3939722804800444,0.08381334692239761,0.06874547394785518,0.3576749369967729,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,33.628233964089304,2.8228933811187744,2.55527400970459,0.8778995387256145,0.0868123191134019,1.1983932331204414,0.38898435949739557,0.09757417067885399,0.07632060471995293,0.35739317489787936,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,41.81650664401241,2.508901596069336,2.3812062740325928,0.8485733307898045,0.0903679718948351,1.1213884204626083,0.4052512622975627,0.08764129132032394,0.07921613099631022,0.3628605059348047,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",100,83.29526677099057,2.283475399017334,2.002208948135376,0.8394474163651466,0.10581216018412895,1.1291093099862337,0.40071753044617453,0.09098709374666214,0.07952833442678055,0.356965241022408,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,34.43057525111362,2.8344454765319824,2.729316234588623,0.8586132861673832,0.09206184910907464,1.210854984819889,0.3999732021681482,0.09944136068224907,0.07635725084602457,0.36277180700562894,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",90,89.37071728194132,2.4107894897460938,2.257204294204712,0.8414903953671455,0.10201823214719524,1.160428449511528,0.40935255111161173,0.08866683393716812,0.07394686027574224,0.7271767870988697,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",100,84.37065713014454,2.35237979888916,2.1582255363464355,0.8470790572464466,0.08832631688004283,1.1237282939255238,0.39061569267933,0.08617522567510605,0.07317826257702298,0.3913135400507599,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",150,133.5542372581549,2.491386651992798,2.339329719543457,0.8530872352421284,0.09034838646058731,1.121402746066451,0.41076305623464493,0.087909996509552,0.07615622704424016,0.7625116480048746,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,36.60404915199615,2.4907898902893066,2.3573904037475586,0.8448160961270332,0.09160807939888682,1.111392930150032,0.39297613484930205,0.08577790856361389,0.07736372272682229,0.4331628391519189,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",90,80.5791030600667,2.3427815437316895,2.1077680587768555,0.8325496390461922,0.10441886663655517,1.1385612022131681,0.41461297409516273,0.08925745636224747,0.07493527574659815,0.46706150006502867,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",100,87.54232358396985,2.2635364532470703,2.1624317169189453,0.8469260185956955,0.09895241030397563,1.1185559928417206,0.4131506130177992,0.0871543437242508,0.07473117634791625,0.4375388359185308,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,37.61112677305937,2.6514666080474854,2.4468748569488525,0.8573003746569157,0.0901384281952645,1.1893379129469395,0.35070370650966076,0.09262825921177864,0.0719744156466396,0.7497112858109176,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",90,72.5544808190316,2.2400765419006348,2.0432915687561035,0.848938524723053,0.10179029762526554,1.2124290950596333,0.39369430536560523,0.09972357377409935,0.07942172543244326,0.5049966520164162,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",140,123.72649318701588,2.234966278076172,1.9961110353469849,0.8403023481369019,0.10518878924954683,1.1567041836678982,0.3965185916065572,0.09059777483344078,0.0745313376254601,0.4227202329784632,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",150,129.93814535299316,2.2244207859039307,2.00488543510437,0.8378928415477276,0.09797890245699888,1.1371959000825882,0.4007483374386316,0.0831935703754425,0.06918008081439832,0.416863442864269,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,84.28535288898274,1.3052363395690918,0.9578210115432739,0.778715256601572,0.09172507318965405,0.7667687628418207,0.3355270807506879,0.040098778903484344,0.04722813217011597,1.0672963859979063,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,4.27007760386914,4.307384490966797,4.379601955413818,0.9346619509160519,0.044121161661145984,1.268564023077488,0.27380221716740094,0.07590527832508087,0.05684832638726169,1.0538623100146651,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,104.71519405697472,1.6751185655593872,1.1961603164672852,0.7781967446208,0.09172768642891642,0.7684333883225918,0.31920489042416594,0.0377819761633873,0.036470213858920845,1.0644126669503748,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",60,128.39730957592838,1.1543467044830322,0.7550449967384338,0.778469055891037,0.08118019388551265,0.706095390021801,0.22637899471179013,0.029661498963832855,0.02472232765151661,1.0584124049637467,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,24.486092213075608,2.2691292762756348,2.123643159866333,0.8399041444063187,0.07150867781957966,0.8312148116528988,0.21404481675451623,0.03902183473110199,0.03189229275566028,1.0841613351367414,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",60,124.35606159991585,1.2828080654144287,0.905607283115387,0.816586185246706,0.09024435764845845,0.7423171941190958,0.27161439371214335,0.03112189471721649,0.024924521214901022,1.0592316698748618,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,4.0885287979617715,4.115971565246582,4.147913932800293,0.9253264926373959,0.04814096408014722,1.1759027801454067,0.31324548036788663,0.07136932760477066,0.05765612971030582,1.0590709228999913,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,26.25780494394712,2.173131227493286,2.166738748550415,0.8439329266548157,0.08587586135727077,0.8493633233010769,0.23716283511653083,0.04259222000837326,0.03376162873390582,1.0544837389606982,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",70,146.6111803711392,1.2000656127929688,0.6153181195259094,0.7528665252029896,0.08764959576042994,0.5985777042806149,0.13930714873956618,0.018106691539287567,0.012892532816966285,1.0622038540896028,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,23.922133922111243,2.150576591491699,2.062023639678955,0.8412487395107746,0.09917788058468813,0.8689641281962395,0.2204886743562996,0.04381514713168144,0.034787092252808405,1.064966832054779,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",60,126.70385997905396,1.40966796875,0.9094955921173096,0.7512503080070019,0.07062121312644258,0.700066827237606,0.19203226212672414,0.029730327427387238,0.02412885247285337,1.062088942853734,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",70,145.34602728998289,1.2012829780578613,0.7284590005874634,0.7660821452736855,0.07183802945321921,0.6748317144811153,0.16128271873118596,0.024895042181015015,0.019260681335980856,1.1176272728480399,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,45.8137582980562,1.672387719154358,1.4706790447235107,0.786009892821312,0.09297758861034176,0.7096519973129034,0.20311916624978218,0.0319332554936409,0.029972648928949755,1.0635828599333763,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",70,149.0954217070248,1.273979902267456,0.8979786038398743,0.7906801179051399,0.11321866198158022,0.5962983965873718,0.15050445636548118,0.01619672030210495,0.014567166256686075,1.0679136370308697,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,44.916231175884604,1.7451493740081787,1.6079670190811157,0.8303542993962765,0.06770422150128418,0.7563819140195847,0.24189162533153288,0.03395436704158783,0.03483075348059619,1.0672111178282648,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,65.20767796598375,1.505951166152954,1.2833590507507324,0.7842102944850922,0.092400588585353,0.7343951407819986,0.21885694465745265,0.03307092934846878,0.027997494895474286,1.065958210034296,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",80,165.91511763399467,1.2112914323806763,0.6255563497543335,0.7531438246369362,0.09008136780595635,0.616402817890048,0.16422827020611766,0.020775146782398224,0.01846346333883628,1.0507524970453233,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",130,266.81170247192495,1.2767791748046875,0.3235727548599243,0.7786544412374496,0.07022245307427943,0.7698254156857729,0.30065386384072096,0.04610317945480347,0.0448561057762096,1.0804738719016314,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,106.69762149592862,1.5559775829315186,1.1207196712493896,0.788210541009903,0.11175061480709882,0.6160889994353056,0.2045711260807234,0.021401964128017426,0.020348357002707398,1.0565083711408079,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",60,124.6172772871796,1.2415765523910522,0.7783606052398682,0.7726118117570877,0.08543505531505734,0.6860550064593554,0.22561642026750095,0.02806071937084198,0.03300668804861507,1.0605083091650158,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",110,225.08541869116016,1.0591791868209839,0.4693096876144409,0.775511059910059,0.06616641780832777,0.7060949541628361,0.2727672886771513,0.02937641739845276,0.028666212171575323,1.0998675201553851,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,4.23316541290842,4.298489093780518,4.366257190704346,0.926238439977169,0.05109776388871423,1.2366612069308758,0.2880117703779806,0.07449857518076897,0.05736105237955332,1.0540357439313084,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,103.400152182905,1.1386854648590088,0.8987850546836853,0.771023090928793,0.08957943678301716,0.6320845130831003,0.1724354231820631,0.023368284106254578,0.027621132846094396,1.0427941700909287,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",60,125.41707545588724,1.3795454502105713,0.8978167176246643,0.7794178202748299,0.05624362630704827,0.6992555912584066,0.22969660314526047,0.028504453599452972,0.022793270792304105,1.0456659959163517,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,44.10559753305279,1.7988927364349365,1.580942153930664,0.8034080006182194,0.09528318986594192,0.7888582795858383,0.23312075250192466,0.04124081879854202,0.04501935995060742,1.0305834270548075,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",80,160.0505551919341,1.1538026332855225,0.5864824056625366,0.7730383388698101,0.07950886680732727,0.7141037303954363,0.3337404554013501,0.033595114946365356,0.04070890462061831,1.0341559229418635,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,99.84750186884776,1.3456275463104248,1.0042517185211182,0.7726154737174511,0.09528037952723471,0.752966208383441,0.27282717785055627,0.03719625622034073,0.03860527549546301,1.701487529091537,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,134.14714185893536,1.3587079048156738,0.8569139242172241,0.7651662342250347,0.08226718708698116,0.7342957500368357,0.26535797863803784,0.034498922526836395,0.031386717732947024,1.718424716964364,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,71.36268436000682,1.6459612846374512,1.4489283561706543,0.8122539296746254,0.07990763102543767,0.757274966686964,0.21643209167067506,0.03384841978549957,0.035301056661300734,1.7037069648504257,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",70,229.0475636969786,1.3167738914489746,0.554909348487854,0.760770246386528,0.06151040007312565,0.6507044434547424,0.2048263492181723,0.028123967349529266,0.03537574658317243,1.6856085329782218,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,166.66246296302415,1.371140718460083,0.7466545701026917,0.7977301813662052,0.07469534481257249,0.6671694722026587,0.14403560803157042,0.024833910167217255,0.018594703243032133,1.6901502760592848,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",60,197.506085942965,1.5155878067016602,0.6365892291069031,0.7745715342462063,0.07746956796662258,0.6506216824054718,0.18543488892985466,0.02055787295103073,0.01725081941907292,1.6890008449554443,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",110,351.7795537279453,5.127638339996338,5.115735054016113,0.9493710845708847,0.03770578364084571,1.4505330100655556,0.40381165945631176,0.09877130761742592,0.08071610156330046,1.687801063992083,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,134.392429479165,1.5726207494735718,0.9691996574401855,0.8020064532756805,0.07904141297145623,0.7118915915489197,0.19806326570147167,0.033963873982429504,0.03954591462844427,1.7140699729789048,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,6.773662213003263,5.126401424407959,5.183925628662109,0.9580385722219944,0.028254367733301786,1.4775657877326012,0.3043867919010019,0.09684866666793823,0.07091813987629787,1.7630125880241394,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,38.837707470869645,2.039046049118042,1.8883051872253418,0.8267195411026478,0.0721099267208733,0.8317387774586678,0.20706718299712162,0.04435979947447777,0.03562459944312452,1.7004595738835633,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",60,194.89177532400936,1.3571269512176514,0.5058111548423767,0.7747838348150253,0.08216186673905178,0.6781946159899235,0.2086260283325231,0.02810857445001602,0.027678968379638465,1.7052277300972492,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,38.083056025905535,2.140549421310425,2.0952327251434326,0.853087242692709,0.06796595206357871,0.8502113744616508,0.21963847279507154,0.04392246529459953,0.03768943053535307,1.7227442159783095,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",60,198.24095413414761,1.2371455430984497,0.535898745059967,0.787199143320322,0.07933394220732184,0.7163018714636564,0.17687475867929642,0.03343112766742706,0.0332348897191462,1.7111048789229244,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",70,229.0000655199401,1.2617805004119873,0.35914620757102966,0.7708149775862694,0.0766057700435372,0.7106912434101105,0.2561248199553598,0.03364916145801544,0.03675242703700808,1.7220656550489366,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,103.82820680108853,1.381287932395935,1.0051668882369995,0.7947988770902157,0.09301918188426135,0.7180995903909206,0.14596848714845537,0.028514429926872253,0.019654009859893174,1.7143792640417814,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,70.44077656790614,1.6844849586486816,1.4568032026290894,0.8258944265544415,0.07269540874157521,0.8002363182604313,0.190039415472587,0.03373271971940994,0.0220793193187028,1.7156160739250481,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",70,229.38798405393027,1.395460844039917,0.5156658887863159,0.7639156393706799,0.054820922502896725,0.6865860298275948,0.3189577863654004,0.02895321696996689,0.0375315227053906,1.700940066948533,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",80,257.5483575409744,1.1916147470474243,0.287185400724411,0.7798452489078045,0.07708155608591646,0.684378944337368,0.18413342417954326,0.029004298150539398,0.02547955790490378,1.6878586141392589,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,38.542503766017035,1.9586734771728516,1.783353567123413,0.861785963177681,0.0656684077091839,0.8457495719194412,0.20133544218247873,0.03927233815193176,0.027387879846218235,1.687086930964142,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",60,194.31931445305236,1.3417795896530151,0.6388282775878906,0.7413600198924541,0.08104934484131822,0.6160352677106857,0.16731388918896098,0.01821804791688919,0.015305115222186735,1.686355629004538,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,102.37358887912706,1.557277798652649,1.2254751920700073,0.8191531263291836,0.08299644922502501,0.7104290649294853,0.280872673789188,0.029142148792743683,0.03360698475939472,1.6986682759597898,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,132.47906927601434,1.320065975189209,0.7628539204597473,0.7906192913651466,0.06943377370713998,0.7004033122211695,0.18818945946756654,0.030345872044563293,0.02471852344896322,1.6855314408894628,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",100,316.9458383580204,1.6208622455596924,0.2518162131309509,0.7679438889026642,0.07522895210792882,0.6792797408998013,0.20451065823283604,0.03147894889116287,0.03555514926172453,1.6454500779509544,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,8.492548013105989,2.576019048690796,2.4113197326660156,0.8620593100786209,0.060781536835842954,0.9768751077353954,0.24587555804424815,0.045137885957956314,0.030318382968440638,16.601043535862118,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",90,19.071537669049576,1.9808075428009033,1.9093918800354004,0.8509504981338978,0.07195012952079256,0.8810147158801556,0.21398242068356088,0.044001173228025436,0.038638933458498795,16.543845718028024,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",100,21.17228103103116,2.116232395172119,2.000577688217163,0.8398740962147713,0.0795990329841447,0.8306418694555759,0.1879598777168872,0.03766165301203728,0.02782407546409279,16.228492069989443,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",150,31.7217375182081,1.2666919231414795,1.0910053253173828,0.8263236470520496,0.08533264693968681,0.8558388967067003,0.23865186131643848,0.0441485159099102,0.04231316970220717,16.60721470299177,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",200,42.30223204335198,1.3914060592651367,1.1695706844329834,0.8172909952700138,0.08197891032456966,0.8513605128973722,0.25988614472279176,0.04558850824832916,0.041689288037582595,16.644927163841203,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,8.531247691484168,2.726689338684082,2.6260221004486084,0.8731404654681683,0.06369409511861522,1.0752068348228931,0.21263659477933286,0.06022971495985985,0.03006383123840451,16.48927926714532,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,10.636380202369764,2.3601925373077393,2.289473533630371,0.8696607649326324,0.0663530603645069,1.0574858710169792,0.27647691603083596,0.06545880809426308,0.045391289384215584,16.3100966589991,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.00151156191714108,6.038139343261719,6.039997577667236,0.9731749072670937,0.017689975805650425,1.8614837527275085,0.3132439233022513,0.16401448659598827,0.10694907840417311,17.07689503603615,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,10.539614273468032,2.426401138305664,2.3347856998443604,0.8514066077768803,0.06840556884691958,1.055913295596838,0.3368287415442618,0.06820335984230042,0.05250552864112928,16.69594048988074,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",100,21.282695239409804,1.3874260187149048,1.3511661291122437,0.8376789726316929,0.07585770123975866,0.9837826266884804,0.2885461131692545,0.06341050192713737,0.0616422237797986,16.558135989820585,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",110,23.387869330355898,2.145125389099121,1.9984865188598633,0.8448193855583668,0.06555732970682661,0.9396579302847385,0.27145303265605436,0.05219990760087967,0.0439831390037335,16.583961837925017,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,6.309478937881067,3.1209847927093506,3.0317883491516113,0.8801568374037743,0.06115685405180675,1.071013119071722,0.3092885429033539,0.06081727147102356,0.049375847156651116,16.046277020825073,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",80,17.03187214047648,2.0250468254089355,1.9638125896453857,0.8435713201761246,0.07389242365859353,0.8862516433000565,0.2233054080388805,0.042677246034145355,0.03214347872985288,16.678060254082084,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",130,27.56423297501169,1.696425437927246,1.6096751689910889,0.8329789340496063,0.08286805636608698,0.943406980484724,0.27047723901166126,0.05617909133434296,0.042935110076431644,16.178509830962867,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.0020039570517838,6.2811479568481445,6.2801642417907715,0.9729611910879612,0.018863805973531537,1.8601756766438484,0.3217271960236108,0.1638164333999157,0.1077358855137684,16.468355785124004,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,10.50443324772641,2.472008228302002,2.420538902282715,0.8664863705635071,0.06418298754853699,0.988285131752491,0.23456864331598773,0.053745947778224945,0.039425428883101944,16.209257904905826,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",100,21.015775967622176,2.310715913772583,2.240050792694092,0.8396864719688892,0.07310917009585081,0.8407565392553806,0.1818646579519048,0.03576362505555153,0.027919298875842925,15.99591106805019,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,2.0959908922668546,3.709904193878174,3.6264562606811523,0.9276150204241276,0.03555360593471878,1.2783502712845802,0.23760947955545336,0.07525090873241425,0.045842352156293745,16.34764204802923,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,4.185539588332176,3.3985767364501953,3.3366551399230957,0.9066462628543377,0.04403256827632393,1.1468100808560848,0.3347647001726971,0.06601400673389435,0.049148686411308005,16.299120951909572,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",70,14.795202681329101,2.479484796524048,2.4244461059570312,0.8541620634496212,0.06487346341647554,0.9653672277927399,0.28942306093943654,0.052194297313690186,0.03962953187986425,16.067823193967342,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,4.18582681636326,2.6985983848571777,2.661510467529297,0.9073161147534847,0.03914754253490642,1.2671894207596779,0.3805934015714947,0.08310054987668991,0.056279264097110815,15.94269382301718,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",80,16.743974392767996,1.9634289741516113,1.8609349727630615,0.8471371345221996,0.08073247151295161,0.9629501476883888,0.2947152744402845,0.056617025285959244,0.046377866109012664,15.876057779882103,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",90,18.837182592367753,1.9951891899108887,1.898627519607544,0.8557148613035679,0.08519447035490524,0.9690551087260246,0.2932104844213732,0.05239769071340561,0.03464020037426332,16.06349125993438,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",140,29.495963560650125,1.9806866645812988,1.7856496572494507,0.8332209028303623,0.08372529559002659,0.9099111594259739,0.35500980750908073,0.05302369222044945,0.050845654129918205,16.00294762616977,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,4.243088043993339,3.538938045501709,3.5124948024749756,0.9002683497965336,0.05708552181521564,1.201761070638895,0.2628416595623979,0.07772517204284668,0.062304499943327846,15.939489769050851,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",70,14.94472124776803,2.291529417037964,2.106264591217041,0.8334334157407284,0.0811493345006839,0.9644650258123875,0.19341228196453422,0.054706159979104996,0.03870674881771781,15.832920713117346,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",80,17.028545289766043,2.212329387664795,2.081393241882324,0.830900177359581,0.07945422333736732,0.9419443681836128,0.22267954110365892,0.05123702436685562,0.03212686960902503,16.42392552108504,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",130,27.64172022580169,1.8844983577728271,1.5987778902053833,0.8250744789838791,0.08860896206562034,0.8948308937251568,0.2355161825660396,0.04266374930739403,0.033257627413368476,16.003043063916266,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,6.252403541468084,3.0140628814697266,2.91839861869812,0.8973353356122971,0.05119937108925617,1.1215642429888248,0.25785137609291936,0.06825487688183784,0.04991291889495525,16.692751199007034,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",80,16.70838824287057,2.9313676357269287,2.7028985023498535,0.8522334434092045,0.0668572119485754,0.94966134801507,0.2874985129246112,0.050037529319524765,0.036333544522788926,14.91480971314013,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",90,18.71939217695035,1.667801856994629,1.3734924793243408,0.8540327325463295,0.06456770204011611,0.9617811106145382,0.2491573090471662,0.05143534392118454,0.03522411498144425,15.025053487857804,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,2.0460773264057934,4.089101791381836,4.04243803024292,0.93047945946455,0.029944348442944,1.3008518926799297,0.2851534921613947,0.08200738206505775,0.06230253959245466,15.754932899959385,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,12.352060977835208,2.4653513431549072,2.318073034286499,0.8748205788433552,0.04848705099756068,1.055097546428442,0.2767354922668858,0.061860788613557816,0.03998118743996751,14.876072745071724,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,2.6393957547843456,3.6887059211730957,3.710477828979492,0.9179089330136776,0.040703888234806196,1.2542355470359325,0.3308156923031707,0.07627423107624054,0.052952105050924764,22.09694233292248,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,5.249980877852067,2.9506382942199707,2.979309320449829,0.906006321310997,0.04895556379235731,1.172682996839285,0.26007985736530986,0.0702129639685154,0.054752126962916314,22.15675566100981,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",70,18.270749567542225,2.2332236766815186,2.141641139984131,0.8726183623075485,0.06462251179704773,0.8981125243008137,0.21695113633475205,0.040702614933252335,0.033514186984050474,21.87193826609291,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,2.6238033620174974,3.7981619834899902,3.6751046180725098,0.9282827749848366,0.03626109502777073,1.394899159669876,0.33931559073532586,0.09837343171238899,0.05135731193107,21.950867263018154,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,15.737863135291263,2.53938889503479,2.425544261932373,0.8644117899239063,0.0701871116202287,0.9675423353910446,0.25450758276599933,0.04715751111507416,0.03450825374586906,21.744100872892886,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",70,18.363506853114814,2.5819554328918457,2.3518941402435303,0.8535147458314896,0.08325731538631272,0.9348277114331722,0.22854414525090216,0.047523416578769684,0.03550146858156004,21.688441222999245,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",120,31.488570059183985,2.2732925415039062,1.9398353099822998,0.8504933938384056,0.0697493521060928,0.912692241370678,0.184241981879833,0.04615848511457443,0.03313249293514062,21.855111191049218,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,13.055106481071562,2.4221014976501465,2.338669776916504,0.8539120517671108,0.07264764862778639,1.027002152055502,0.3470520750514992,0.06596226245164871,0.06220670451418316,21.684912653872743,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",100,26.105210786219686,2.4038243293762207,2.0693793296813965,0.8410028927028179,0.07734530789270185,0.9582264944911003,0.27332956181835877,0.058566901832818985,0.05748948769425695,21.87781075690873,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",110,28.713310488965362,1.8989990949630737,1.7017123699188232,0.8382840380072594,0.07825633509282622,0.9612301029264927,0.2566572484804077,0.06617187336087227,0.0822528631085379,21.905528913950548,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,2.6344996269326657,3.611708641052246,3.599984645843506,0.9201065972447395,0.04002985883726929,1.2969676293432713,0.33083919185975125,0.08784713223576546,0.072381674761688,21.959450450958684,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,15.76370951719582,2.173405408859253,2.104979991912842,0.8453363366425037,0.06908904785249118,0.9286224693059921,0.30352664716447786,0.04658770188689232,0.043153458115317525,21.84192560100928,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,5.223336782306433,3.3052666187286377,3.207557201385498,0.9093315079808235,0.0438724857671223,1.2040855810046196,0.25977733990093765,0.07426781579852104,0.05949891630620619,21.364038373809308,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,10.438250138191506,3.234368085861206,3.1110196113586426,0.8761913329362869,0.05836228457685401,1.0410185270011425,0.34476687731189226,0.05672695115208626,0.048577522626949435,21.680813706945628,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",90,23.499442072585225,2.0865859985351562,1.936422348022461,0.8616043739020824,0.05820996196991254,0.929113045334816,0.2561599262279142,0.0442236065864563,0.03171654411832586,21.51332138106227,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,10.44559167465195,2.293238639831543,2.234682083129883,0.8859569728374481,0.05149070917010395,1.0273852404206991,0.29578151763805044,0.055600300431251526,0.04761301829667818,19.054113235790282,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",90,23.58285508211702,2.413677453994751,2.2559003829956055,0.8546152859926224,0.07550976959076212,0.9120123349130154,0.17018612671823047,0.04282527416944504,0.03403903888177169,18.683185607893392,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",100,26.194111060816795,2.535148859024048,2.375251531600952,0.8300451710820198,0.08300165387009545,0.872919850051403,0.18348866353741555,0.041544798761606216,0.02807881106504167,18.577837272081524,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,12.980939789908007,2.424541473388672,2.3441054821014404,0.8741435892879963,0.0595471270392555,1.0631284527480602,0.39353908827202333,0.06422749906778336,0.0637439610499087,18.45331888203509,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,2.601965078385547,4.075817108154297,4.049365520477295,0.9143386669456959,0.037844014876657076,1.1932073310017586,0.2524558469629082,0.06956009939312935,0.04780784053594465,18.72114914096892,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,15.613567302236333,2.4363174438476562,2.3528823852539062,0.8583691567182541,0.06678882504359952,0.932481799274683,0.2600257224702811,0.05126577243208885,0.04614048285144064,18.731368002016097,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",70,18.22124963416718,2.606644630432129,2.4008686542510986,0.8433813862502575,0.07809811516593937,0.8842426016926765,0.2109491985925141,0.04127892479300499,0.031058244478125475,18.760653750039637,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,10.546808832325041,2.764564275741577,2.6555442810058594,0.8724385052919388,0.05498167403067444,0.9714403487741947,0.23797154685740513,0.04378877207636833,0.029332006218122153,18.77772554103285,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",90,23.590111784636974,2.1922178268432617,2.0164501667022705,0.8596529327332973,0.05376880750986612,0.9151754565536976,0.23674617201923834,0.04078405722975731,0.03360303152904203,17.590889901854098,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,10.396693579154089,2.7019901275634766,2.5175085067749023,0.8983128741383553,0.04237312869504253,1.1373377740383148,0.2942759104234512,0.0659698024392128,0.05171562154356483,18.639071258017793,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,13.002631681971252,2.5660598278045654,2.3853747844696045,0.8917492851614952,0.04654646866660803,1.1262779980897903,0.31674516929097485,0.07348166406154633,0.061080036113066186,18.366485120961443,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",100,26.112329608062282,2.593519449234009,2.245800018310547,0.8744495064020157,0.05420986130492205,1.0607487969100475,0.3155812015871309,0.06255057081580162,0.04859732494960992,18.5528515400365,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","slcp[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=16384]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",150,39.125668396707624,2.8597359657287598,2.371659994125366,0.8597139120101929,0.06652046366408401,0.9659288302063942,0.30199594442541716,0.04994230344891548,0.03815699146460618,18.626814514864236,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",0,0.0012929011136293411,2.9297568798065186,2.8923728466033936,0.986847847700119,0.0037523742648354038,1.4609136059880257,0.2921764700088369,0.3251142371445894,0.20996692576974638,2.1548998709768057,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",10,0.1508360409643501,1.0936325788497925,1.0848907232284546,0.9577007368206978,0.022928003402818503,0.7312321849167347,0.20704448597197736,0.20256248489022255,0.19085946369190804,2.255361116025597,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",70,0.9167551207356155,0.12262750416994095,0.07970970869064331,0.8986511565744877,0.04425227234170035,0.41899267863482237,0.14363683970205174,0.0795189831405878,0.08927115441632524,1.8963494000490755,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",120,1.5684189449530095,-0.6873734593391418,-0.7606571912765503,0.7883945107460022,0.03937753165825911,0.17626673076301813,0.04406072576028616,0.025271166115999222,0.042769610282794635,2.41748539195396,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",170,2.2185816417913884,-0.9321469068527222,-1.0033572912216187,0.7333031222224236,0.07910898349711346,0.1669185501523316,0.059334341772373414,0.024917539209127426,0.036387609596895076,2.524132716935128,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",180,2.3487694661598653,-1.0222620964050293,-1.0883978605270386,0.7291022874414921,0.05841763471470366,0.11926205456256866,0.029021797282397982,0.01768803969025612,0.038267858034432375,2.684947242960334,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",230,3.0168726502452046,-1.172219157218933,-1.1811652183532715,0.7216166853904724,0.08284510718926247,0.1517814437393099,0.047688347720455763,0.01664729416370392,0.028540140640899322,2.6043797791935503,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",10,0.12992521678097546,1.0674856901168823,1.0635654926300049,0.9569994397461414,0.020504188518936713,0.7153666242957115,0.19896503905931698,0.1950781363993883,0.1846807101268781,1.6753920011688024,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",70,0.9079259736463428,-0.30035749077796936,-0.3161836564540863,0.8636210449039936,0.0360280133582146,0.28322378639131784,0.07676321602833172,0.043246619403362274,0.046727602126734456,2.0554220699705184,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",80,1.0359847738873214,-0.4570417106151581,-0.5373876094818115,0.83886643871665,0.02666355805125337,0.20718718878924847,0.03868140273436062,0.027354411780834198,0.03739105902932355,2.1263962350785732,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",130,1.673708266345784,-0.9476418495178223,-1.0012927055358887,0.7355691306293011,0.06047343119778379,0.15856043668463826,0.043115922393743525,0.016091998666524887,0.026929563580668284,2.324711050838232,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",180,2.311260227812454,-1.0704060792922974,-1.106581211090088,0.7260377034544945,0.06493757199445087,0.17589733423665166,0.06358588106862098,0.017039820551872253,0.025318175995392937,2.6306852761190385,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",230,2.9514144125860184,-1.031569242477417,-1.137355089187622,0.705780167132616,0.06486748402641979,0.1270250240340829,0.044535955297583835,0.015444479882717133,0.024945791457474418,2.736270071938634,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",280,3.6003650720231235,-1.178695797920227,-1.2396793365478516,0.6725803539156914,0.07978349409768114,0.12452997150830925,0.04677154321244939,0.01572646200656891,0.02448974955239408,3.5697320019826293,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",300,3.8722687617409974,-1.2082624435424805,-1.2581872940063477,0.6994646340608597,0.08371939894770627,0.15918764541856945,0.07745252456621007,0.015220385044813156,0.03057515882460716,2.690063130808994,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",10,0.12770128366537392,1.078357219696045,1.0637943744659424,0.9566040709614754,0.019280192564705503,0.7245080471038818,0.17653196255150946,0.19283833354711533,0.18204497844573597,1.7785687358118594,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",60,0.7758576332125813,-0.10973268747329712,-0.1310933530330658,0.8889145180583,0.0461584138255519,0.3528319811448455,0.11842394141898768,0.062168387696146965,0.06698396377795159,1.854566473979503,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",70,0.9034785609692335,-0.3507176637649536,-0.4088013172149658,0.8606553822755814,0.03817693922574329,0.24453605758026242,0.05664420851496828,0.03771762177348137,0.0443790790625239,2.0044026151299477,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",120,1.5466561545617878,-1.0137939453125,-1.0494141578674316,0.7469456233084202,0.05873098873652839,0.15543522546067834,0.037052403539702114,0.024881914258003235,0.05244859023575754,2.3256491930224,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",0,0.0016200970858335495,2.8700380325317383,2.850294351577759,0.9870602190494537,0.00377020406407499,1.4949013516306877,0.3505847789652204,0.3371251281350851,0.20092549100186638,1.2470091809518635,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",50,0.6220121530350298,0.28629711270332336,0.2662963271141052,0.909025140106678,0.07189821352109467,0.4716058960184455,0.1710209729437752,0.09563743695616722,0.10958894193092429,1.8940201851073653,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",60,0.7483904191758484,0.19065631926059723,0.1458737850189209,0.9061357341706753,0.05623335120059399,0.43056849762797356,0.1614782885402796,0.08530083112418652,0.09571743077970626,2.0018553039990366,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",110,1.3715838054195046,-0.672035813331604,-0.7092956900596619,0.8102969042956829,0.039969710061268635,0.19056513905525208,0.046296886726317765,0.024194225668907166,0.036222482872611046,1.8035077389795333,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",160,2.0101560847833753,-1.0169509649276733,-1.0680789947509766,0.7113990820944309,0.07818749362719513,0.21084106131456792,0.09426640555377552,0.01781998574733734,0.02124637010038936,2.467763272114098,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",10,0.1278974488377571,1.0206159353256226,1.0109106302261353,0.9558113142848015,0.022062740679132848,0.684787979349494,0.19567435433966848,0.18411901593208313,0.17715443658957217,1.710909394780174,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",30,0.37815498607233167,0.46049198508262634,0.45898956060409546,0.9350876696407795,0.029972822394049213,0.5325559731572866,0.18725998179876943,0.12302893027663231,0.13376469095797197,2.060834954958409,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",80,1.0122918752022088,0.050891485065221786,-0.010606941767036915,0.890353549271822,0.07123017507741324,0.35069908387959003,0.11157676447000837,0.06914239749312401,0.07841841288837045,2.0339801032096148,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",130,1.6355692385695875,-0.7040725946426392,-0.7211580276489258,0.7990112788975239,0.03437563320051073,0.1975353960879147,0.04981759754444364,0.02226085215806961,0.028820330665468644,2.169350323965773,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",180,2.261873106705025,-1.035828709602356,-1.0592180490493774,0.7205183729529381,0.07388260684219744,0.12345235119573772,0.03389033805061951,0.01601320132613182,0.027537574608190726,2.42520024580881,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",190,2.400664640357718,-1.025275468826294,-1.1120797395706177,0.7229907847940922,0.06817007142043995,0.1433107308112085,0.05312452011065494,0.013788856565952301,0.0173506215026958,2.2962816418148577,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",240,3.0671178072225302,-1.0664637088775635,-1.1209708452224731,0.7221711911261082,0.06285004680860183,0.13173337513580918,0.04094795987663243,0.01465630903840065,0.03146471637600469,2.3725376250222325,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",50,0.6451133694499731,0.01141722034662962,-0.023937392979860306,0.8934913352131844,0.04038775008649852,0.36462924629449844,0.11370673837952025,0.06808868050575256,0.07051817512771019,1.8231855551712215,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",100,1.292823291849345,-0.7981762290000916,-0.8918273448944092,0.7569511421024799,0.06667845437052063,0.25543219689279795,0.09545494277965093,0.032213203608989716,0.040973280678915217,2.106160511029884,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",110,1.4206080983858556,-0.9275475740432739,-0.977902352809906,0.7551013827323914,0.06275928578903213,0.19549527578055859,0.09692743777238448,0.02066364511847496,0.028083364071581043,2.209402129985392,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",160,2.038582512876019,-0.9087424874305725,-1.0029489994049072,0.751283623278141,0.05356908473790553,0.16536408802494407,0.049532671970129415,0.01830393448472023,0.021558631919080938,2.4877876942045987,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",210,2.656312831211835,-1.1378953456878662,-1.137202262878418,0.7164678908884525,0.07069496755310135,0.14719129307195544,0.061132082413517995,0.017503295093774796,0.032871272952682426,2.5874877760652453,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",260,3.2832380023319274,-1.0492165088653564,-1.1903704404830933,0.7130379676818848,0.07654028697395651,0.14679702930152416,0.054816187218689846,0.023089207708835602,0.037050253742881366,2.7771068119909614,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",10,0.12604188779368997,1.0415654182434082,1.0422064065933228,0.955413781106472,0.023044099273641906,0.7204415053129196,0.19814451937642732,0.19231455959379673,0.1816066120368178,1.614261036971584,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",60,0.7395944893360138,0.03527078405022621,0.024135032668709755,0.8984972313046455,0.03652337925085102,0.3892957055941224,0.1279943804143228,0.07410484179854393,0.08215723918343092,1.7940960379783064,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",110,1.3558754562400281,-0.7801323533058167,-0.7535663843154907,0.7960140705108643,0.04298729121585399,0.19887094711884856,0.08272172169410358,0.020221248269081116,0.026519497271392904,2.3565729141701013,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",160,1.9633977124467492,-1.0056508779525757,-1.0836857557296753,0.7022413350641727,0.08345174171104214,0.13362109707668424,0.05549058504084063,0.015985839068889618,0.029965902288873587,2.7165061850100756,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",170,2.0857243223581463,-1.1008625030517578,-1.1088380813598633,0.7005582451820374,0.06588660818967022,0.12399781821295619,0.04585120997669718,0.01502501592040062,0.032842969779217315,2.7128301390912384,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",0,0.0018883422017097473,2.8540873527526855,2.845547676086426,0.988922655582428,0.0026834692610341426,1.523384802043438,0.34548050874376995,0.34583344496786594,0.19962201039180003,1.2028159250039607,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",70,0.8626007956918329,0.010336998850107193,0.005852687172591686,0.8973713405430317,0.0402122643737573,0.3796713463962078,0.1281509811411796,0.0714923869818449,0.07682360880487905,1.7916618569288403,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",120,1.4715911734383553,-0.9114904403686523,-0.8827344179153442,0.7570168934762478,0.06992498378577075,0.16220323415473104,0.04134266774886335,0.023369960486888885,0.04473732806940487,2.02276011207141,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",130,1.5916261293459684,-0.7184174060821533,-0.8178204298019409,0.7553670443594456,0.07221510260396732,0.17151686968281865,0.04469910559349941,0.024244394153356552,0.027488054670144597,2.0393474500160664,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",180,2.176664396887645,-0.9608291387557983,-1.0413014888763428,0.7347092106938362,0.0662041694206015,0.1284248768351972,0.025647573765961596,0.011125244200229645,0.014920289011719582,2.2015378361102194,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",40,0.4855608136858791,0.3008997440338135,0.2966822683811188,0.9201364107429981,0.03721337821542282,0.5122528187930584,0.20544369703057994,0.11445206589996815,0.1243793151763967,1.97070545097813,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",100,1.1997595655266196,-0.3761461675167084,-0.3825550377368927,0.8488190993666649,0.04978606534633097,0.2669605631381273,0.07428319448441774,0.0362112894654274,0.03776345674904493,1.9774661420378834,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",110,1.3162636568304151,-0.6109662055969238,-0.6357958316802979,0.8102743849158287,0.05607511243208104,0.19420406548306346,0.04291031128740268,0.029593709856271744,0.0396247627717082,2.070049164816737,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",160,1.894942267332226,-0.9537418484687805,-0.9461090564727783,0.7647424638271332,0.04056301693463816,0.15195747651159763,0.04765040427041354,0.015610534697771072,0.023394230307007453,2.062978647882119,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",210,2.4723813044838607,-0.9476134777069092,-1.0000290870666504,0.7260774374008179,0.05588672385158924,0.14704751456156373,0.045432033450955686,0.012100819498300552,0.015556068003392712,2.2342547678854316,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",40,0.4582757137250155,0.3368900716304779,0.3375634253025055,0.9265432581305504,0.035143924974694506,0.5061501283198595,0.1861707961283665,0.10974199883639812,0.12226495423076952,1.729682972887531,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",90,1.0327333479654044,-0.4990045428276062,-0.559669017791748,0.8389847315847874,0.02138813821097596,0.21355232456699014,0.05657763084758835,0.026482630521059036,0.03737699243329751,2.1025849049910903,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",110,1.2601707582361996,-0.7375975847244263,-0.8116605281829834,0.7866875641047955,0.046398426694053585,0.21078643901273608,0.08607557643523525,0.028525684028863907,0.03649195020456981,2.036483753938228,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",160,1.8292999416589737,-1.057483434677124,-1.115794062614441,0.7205455787479877,0.07682567256990679,0.13896489050239325,0.05620790909933455,0.025697115808725357,0.047171150751985864,1.8440086671616882,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",10,0.16427554516121745,1.079854130744934,1.0795797109603882,0.9554139263927937,0.019322614293248396,0.7266609538346529,0.20534887668677906,0.1562857385724783,0.160289158367164,1.4505666610784829,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",20,0.32376087736338377,0.5592937469482422,0.5027906894683838,0.9270622916519642,0.052640701693813255,0.5909860953688622,0.21744031531264454,0.11414708755910397,0.12870427289083078,1.6381110029760748,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",70,1.149684763746336,-0.5941786766052246,-0.750482976436615,0.7835801914334297,0.049256101919559536,0.18802725803107023,0.04923097184360199,0.02856871858239174,0.04236815074159725,1.8479454470798373,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",120,1.9855342947412282,-1.01885187625885,-1.059295654296875,0.7303449884057045,0.052213466337365504,0.14818761218339205,0.04169279495838689,0.018009893596172333,0.028340153851982595,2.1331208359915763,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",30,0.48960413853637874,0.5384246706962585,0.43230971693992615,0.9283143803477287,0.03761833392320099,0.5060878274962306,0.1971171551366063,0.09357913583517075,0.11413969210641971,1.7166945880744606,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",40,0.6502874114084989,0.16721570491790771,0.20502349734306335,0.8973105065524578,0.08882871901835471,0.44222011882811785,0.16079674757736953,0.07772479206323624,0.08973155942262799,1.597222587093711,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",90,1.4635012322105467,-1.0377432107925415,-1.0892465114593506,0.7236948385834694,0.06625364634635406,0.18782681180164218,0.069005885383008,0.016421295702457428,0.027168804739670042,2.0811432311311364,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",10,0.16373745258897543,1.0132685899734497,1.0060665607452393,0.9545595273375511,0.020577871076281826,0.7257222421467304,0.19050400729626768,0.15687190368771553,0.16054979241180664,1.5716579060535878,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",60,0.9819315182976425,0.11320846527814865,0.0360274612903595,0.8984677083790302,0.04049004103432175,0.376930414699018,0.12321119528383878,0.0620485283434391,0.0726611115638516,1.6047507009934634,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",110,1.8024841679725796,-0.90645831823349,-0.966137170791626,0.7456029653549194,0.07069074813595423,0.13345473865047097,0.028133302696946902,0.015472754836082458,0.0288617684631965,1.9211214000824839,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",130,2.1218821147922426,-1.0967473983764648,-1.1259746551513672,0.7188099324703217,0.06912206476908794,0.13604795234277844,0.04611781856050455,0.0165153369307518,0.03755064873096075,1.991610178956762,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",180,2.92881808266975,-1.1369746923446655,-1.1822872161865234,0.7130102440714836,0.09228235962844603,0.14335893746465445,0.06575988470907815,0.012743942439556122,0.03113405662776857,2.3363798689097166,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",230,3.729262900771573,-1.0848610401153564,-1.2068862915039062,0.6735538803040981,0.09331433882521721,0.17516055051237345,0.06935354790733661,0.022006560117006302,0.045565294343503114,2.322623637970537,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",0,0.0019152010791003704,2.855560302734375,2.831929922103882,0.9876705668866634,0.0037037635224005123,1.4289238341152668,0.3028776737768993,0.2741209175437689,0.21497690587735094,0.9526336779817939,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",50,0.8292749421671033,-0.08278760313987732,-0.10765111446380615,0.8827555365860462,0.03985660602286512,0.3558208681643009,0.10053852445560425,0.055761780589818954,0.06371205104617435,1.7193909990601242,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",90,1.4931224675383419,-1.10330069065094,-1.1425702571868896,0.7169566601514816,0.06527038631077299,0.13565665436908603,0.07758905536921017,0.012035820633172989,0.02214936280437047,2.4141349599231035,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",160,2.6502777794376016,-1.1072933673858643,-1.1878135204315186,0.6566549837589264,0.07401069474675517,0.12644560541957617,0.04911335796564549,0.009296264499425888,0.01665022587770455,2.4097043010406196,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",0,0.002786880126222968,2.888472080230713,2.8590683937072754,0.9879158139228821,0.004211359556861184,1.4990787766873837,0.34969944359503097,0.28413438983261585,0.20887617618898977,0.7720014390069991,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",50,0.8259387810248882,0.3297322392463684,0.2873583436012268,0.9065223038196564,0.08541725687370522,0.46898213867098093,0.1711802699151995,0.09015609323978424,0.10649932100510076,1.5274680030997843,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",100,1.629163209348917,-0.7614072561264038,-0.7952474355697632,0.7837448641657829,0.04678112974767611,0.17724020034074783,0.04641104046599576,0.021748192608356476,0.03883391827855417,1.9501722329296172,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",150,2.454289611428976,-1.024470329284668,-1.0959972143173218,0.7095914334058762,0.059733338939878,0.12336806301027536,0.032314334704948634,0.014931585639715195,0.027442661211330526,2.496143377851695,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",0,0.0019456190057098866,2.8614890575408936,2.850517749786377,0.9883099943399429,0.0039916672043091,1.487355601042509,0.33287692679025926,0.2853755857795477,0.21174083235294103,0.7535329801030457,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",50,0.8282914182636887,-0.14979110658168793,-0.13311368227005005,0.8553758375346661,0.08466323551696951,0.3096479997038841,0.07938721354682302,0.05047769472002983,0.05485328106675757,1.6505265799351037,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",100,1.6374338681343943,-0.8333922028541565,-0.9437755346298218,0.7299522161483765,0.08279446463141217,0.1599649996496737,0.07461657088468451,0.01313350722193718,0.01616508654079268,1.9825163530185819,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",150,2.4504003797192127,-1.0119962692260742,-1.1494035720825195,0.698976680636406,0.0791463017209316,0.20473184762522578,0.1095280789238858,0.011813607066869736,0.011826503481415132,2.1832296948414296,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",160,2.6110896023456007,-1.0259052515029907,-1.1063823699951172,0.7214696742594242,0.07854417087020987,0.1543892645277083,0.06350761168526364,0.013307314366102219,0.023440993635606817,2.2538921509403735,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",40,0.6425291122868657,0.27558356523513794,0.2292991578578949,0.9192842356860638,0.03546167434161922,0.4740077592432499,0.18628700958000405,0.08722687140107155,0.1023284448268735,1.8244044410530478,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",100,1.6355869211256504,-0.9699599146842957,-1.059731125831604,0.7055051773786545,0.0791743037140324,0.14492490934208035,0.053956875833917596,0.021277934312820435,0.04054972082843984,2.3410806390456855,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",150,2.4365485773887485,-0.9136059284210205,-1.0158823728561401,0.7450025305151939,0.050202274714192835,0.16939103556796908,0.07144676993753328,0.01580549031496048,0.022984063860761784,2.366316047962755,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",0,0.002216106979176402,2.851818799972534,2.8343892097473145,0.9880981408059597,0.0045682860317936705,1.479906890541315,0.3373217106205441,0.2839562464505434,0.2168324883244361,0.8300761741120368,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",50,0.8069847265724093,0.0483030267059803,-0.022603336721658707,0.8807569742202759,0.06096220476572364,0.34955441672354937,0.10554496994287915,0.051012393087148666,0.05689899581826829,1.6782403029501438,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",100,1.6251726194750518,-0.9576396942138672,-0.9982063174247742,0.7391211465001106,0.07060479365478467,0.13583076209761202,0.043239587595867116,0.010200150310993195,0.015742074845983875,1.9867262591142207,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",150,2.418391432845965,-1.1420814990997314,-1.2428070306777954,0.6814201772212982,0.07907509056900176,0.1574893514625728,0.06545541493489722,0.00858917087316513,0.009774415107547873,2.194450818002224,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",170,2.7341589445713907,-1.1842713356018066,-1.2519140243530273,0.7138308137655258,0.0681660450410993,0.12158617936074734,0.04342378044488813,0.007862739264965057,0.015039020034365549,2.0792097819503397,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",10,0.15905260876752436,1.045574426651001,1.0621464252471924,0.9558099694550037,0.024413513482413513,0.7141039110720158,0.22137747145097134,0.1590820774435997,0.16489360211742188,1.5126030270475894,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",60,0.9426711711566895,-0.271420955657959,-0.30670931935310364,0.8710336796939373,0.0348507384224451,0.3001105794683099,0.09833218610538286,0.032867804169654846,0.039059400807553726,1.724258598871529,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",110,1.7345971481408924,-1.011892557144165,-1.0884120464324951,0.7169226668775082,0.07966684881761812,0.16378449625335634,0.05420823869793805,0.01586487889289856,0.02541090647728237,2.026870714034885,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",120,1.8893139427527785,-0.9729318618774414,-0.9828986525535583,0.7239977046847343,0.08824438936267374,0.15681779570877552,0.06871134925809812,0.017723724246025085,0.03171165079656751,2.0136072079185396,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",160,2.5109751797281206,-1.0505940914154053,-1.1566780805587769,0.6849387958645821,0.06843777045400228,0.1337208435870707,0.04859808406997516,0.009752534329891205,0.01891906236739531,2.1839513960294425,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",50,0.8030138306785375,0.01643933355808258,0.029459340497851372,0.8826607912778854,0.09504200930077211,0.38085214234888554,0.12984153347917177,0.05476630851626396,0.06276235462374935,1.8391121230088174,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",100,1.57162768044509,-0.8882964253425598,-0.9260578155517578,0.7344448156654835,0.07531837652545344,0.14537752140313387,0.03547638368232316,0.017008166760206223,0.026408507480541812,1.8777359779924154,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",110,1.7253671172074974,-0.8184981346130371,-0.8435136079788208,0.7780750244855881,0.05961783227480455,0.18119036173447967,0.05944790924163135,0.026304442435503006,0.048118891179572756,1.9371333071030676,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,0.5943148822989315,0.22851642966270447,0.15485771000385284,0.9111621864140034,0.04314239637733155,0.5077171493321657,0.21860077519352542,0.1127437949180603,0.12112938233123138,0.016929453937336802,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",90,1.3260154796298593,0.07172790169715881,-0.03176288679242134,0.896304190158844,0.04582813564810757,0.4799011927098036,0.22053706949996765,0.09780684858560562,0.1081332858085317,0.0166078619658947,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",130,1.8914753207936883,0.031087424606084824,-0.10044625401496887,0.8849493935704231,0.0524529391448025,0.468347049318254,0.22119845555696832,0.09014778584241867,0.0984877211860563,0.017178071895614266,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",160,2.2966590283904225,-0.02209441363811493,-0.15221011638641357,0.8906871303915977,0.04583720869919754,0.475815299898386,0.22417384149257696,0.08877978660166264,0.09860360804301271,0.016938531072810292,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",210,3.0158911566250026,-0.07808157801628113,-0.21378080546855927,0.887974463403225,0.049892295220030546,0.47465829364955425,0.22718411990911216,0.0867924615740776,0.0948658226280397,0.017011076910421252,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.13346270890906453,1.2854784727096558,1.3248159885406494,0.9675307013094425,0.018263679801041863,0.7502111904323101,0.1729619853189753,0.1873243749141693,0.1718572773780315,0.0172094798181206,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,0.7857820512726903,0.15709367394447327,0.05386409908533096,0.8919379450380802,0.08119544547469205,0.48105297796428204,0.21935443518860337,0.09479953162372112,0.10257955640193721,0.017096216790378094,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",110,1.4105381064582616,0.04088173061609268,-0.08529239147901535,0.8855191916227341,0.06645561417076895,0.4748644335195422,0.22137034738672495,0.08798366598784924,0.09632194951655502,0.016939441906288266,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",130,1.6588977524079382,0.018325667828321457,-0.1188102662563324,0.890655156224966,0.04662637736559312,0.4716591006144881,0.22469887586594856,0.08536826446652412,0.09365393798754794,0.016960281878709793,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",180,2.3128473155666143,-0.048473335802555084,-0.1869199126958847,0.883423212915659,0.05492643180244669,0.4814375061541796,0.23450087870256742,0.08793563582003117,0.09375416042617116,0.017166990088298917,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.0033389071468263865,2.944843292236328,2.901383399963379,0.9881605356931686,0.004133560239496383,1.5455683767795563,0.36542917354367904,0.33066850528120995,0.21428829301655436,0.016846199054270983,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.14190913829952478,1.324119210243225,1.3435578346252441,0.9657597355544567,0.02577404774401392,0.7420720066875219,0.21230547168725775,0.18374602682888508,0.1730462452004777,0.017437648959457874,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,0.8535899990238249,0.1409703642129898,0.03954632580280304,0.9006677493453026,0.04448086638552968,0.49264330323785543,0.23740397114356923,0.0975202564150095,0.1073977026543477,0.017018442042171955,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",110,1.5277787507511675,0.05686548352241516,-0.082709901034832,0.8934949114918709,0.05090331895762278,0.492030156776309,0.24291625018997448,0.08612256497144699,0.09291697575754679,0.01704764598980546,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",170,2.311596616404131,0.012918581254780293,-0.1511518955230713,0.8820183090865612,0.06564483717455709,0.4941531531512737,0.2521336755781638,0.08856947533786297,0.10011546854921927,0.016869646962732077,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",180,2.4443493850994855,0.000966319814324379,-0.16255176067352295,0.8910214528441429,0.0540631558371459,0.4896695567294955,0.24542119043438662,0.08391222730278969,0.09461923212258247,0.016727139009162784,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",230,3.119646516162902,-0.04446981102228165,-0.22028006613254547,0.8781147450208664,0.058371872020617124,0.47407650761306286,0.2422403405176027,0.07971446961164474,0.09238649394314007,0.019002154003828764,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.13343734992668033,1.503570556640625,1.53620445728302,0.9729603789746761,0.017306259243179203,0.8312265872955322,0.21482226994196435,0.20398335717618465,0.17990473230083415,0.017692717956379056,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,0.7820626357570291,0.16900283098220825,0.05852445587515831,0.902004137635231,0.041453294133326136,0.48351739533245564,0.22448954666029414,0.10066784732043743,0.10850128039535493,0.017236382933333516,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",110,1.4328555597458035,0.049828507006168365,-0.10231691598892212,0.8837669230997562,0.058236468283653524,0.472600013948977,0.2303931170442501,0.09167228639125824,0.09982886664984938,0.017069226829335093,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",130,1.6920759384520352,0.012928074225783348,-0.1448325216770172,0.890071339905262,0.04487007943728286,0.48035016003996134,0.2333111578312756,0.08834360912442207,0.0974303638455787,0.017200140981003642,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",180,2.3460569428279996,-0.028545178472995758,-0.20950257778167725,0.8705127015709877,0.0785556840633213,0.4645135682076216,0.22680412315894294,0.08378208056092262,0.092724452588174,0.01756323385052383,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",230,2.9913679282180965,-0.09735078364610672,-0.27525556087493896,0.869810663163662,0.05258986138237414,0.4618282364681363,0.23483681978884746,0.08343181759119034,0.09322622586962533,0.017239337088540196,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",240,3.1206479389220476,-0.1076589822769165,-0.29591232538223267,0.8771348185837269,0.054862396508262606,0.4717951971106231,0.24514899251408098,0.08343635499477386,0.09404158007898633,0.017124792095273733,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",290,3.7638287290465087,-0.15209180116653442,-0.3337562084197998,0.8738064281642437,0.054933680935109926,0.47084297612309456,0.2449518798605703,0.08228194713592529,0.09290722197093333,0.01719724596478045,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",330,4.283040805021301,-0.16158299148082733,-0.35985100269317627,0.8588527925312519,0.08950472757042802,0.4648025562055409,0.2512637331386051,0.0814887247979641,0.09332961799572786,0.017075669951736927,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",400,5.187653357861564,-0.21852204203605652,-0.4278924763202667,0.8608084097504616,0.07695156728658378,0.4661219916306436,0.25573295151938935,0.0794260073453188,0.09244217679796703,0.017236347077414393,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",410,5.320625403430313,-0.22859594225883484,-0.4371652603149414,0.8617580011487007,0.06672203338386785,0.46291172970086336,0.24381989579018237,0.07627941481769085,0.08681040012658355,0.01709644985385239,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",460,5.96280675358139,-0.25087955594062805,-0.45009446144104004,0.8650535158813,0.06629287000911499,0.46178647922351956,0.24793304394391483,0.07678556442260742,0.0887289300335353,0.017094722017645836,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",510,6.605013070162386,-0.25665947794914246,-0.4707157611846924,0.8613273724913597,0.07171657352813804,0.45943871745839715,0.2539149966939492,0.07719373516738415,0.09111458210934514,0.01708461600355804,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",560,7.254018250154331,-0.27253928780555725,-0.4814547896385193,0.8586103059351444,0.07836587927164035,0.45950411492958665,0.24852299049426096,0.07710135728120804,0.08795933559157398,0.01718514203093946,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",600,7.774324131896719,-0.29870355129241943,-0.5109896659851074,0.8574849404394627,0.08080808236877672,0.4683881192468107,0.2603144521551776,0.0757832769304514,0.0892129800998263,0.017105862963944674,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.0024311018642038107,2.9182260036468506,2.899972438812256,0.9875489175319672,0.003951337018575546,1.5990400463342667,0.3819449948601701,0.32740399800240993,0.2133580538345613,0.016787996981292963,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,0.6502980380319059,0.1615833342075348,0.06948824226856232,0.9032601043581963,0.037532255046641996,0.5022195382043719,0.22485408621933411,0.10731447488069534,0.11350667132965864,0.016512410016730428,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",100,1.29278371646069,0.05637865513563156,-0.061449259519577026,0.8954144604504108,0.04388168768465739,0.5021947184577584,0.24399805537330377,0.09888529218733311,0.10504110501425849,0.016685371985659003,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",110,1.4220066650304943,0.034149859100580215,-0.08933807909488678,0.8962104916572571,0.046369176423901017,0.5016644177958369,0.2390691880921389,0.09571602754294872,0.10236347987123946,0.016688218107447028,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",150,1.9346064580604434,-0.04401880130171776,-0.16724050045013428,0.8823838718235493,0.04504618772827901,0.49077373277395964,0.23557718919760434,0.09407593682408333,0.10416074033886513,0.016578231938183308,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",220,2.8331307508051395,-0.10931002348661423,-0.2334476262331009,0.8796120099723339,0.06973715285760483,0.48349198140203953,0.23849865476035767,0.08942248113453388,0.0964707237919725,0.0165975799318403,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",270,3.4699839160311967,-0.15611016750335693,-0.28885236382484436,0.8677667826414108,0.0905264996232384,0.48819343000650406,0.24893169730446688,0.08862142078578472,0.0985923598928402,0.016451586037874222,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",280,3.5972168669104576,-0.12251153588294983,-0.2595122754573822,0.86951569840312,0.08366411380661448,0.48237926326692104,0.2329746193030458,0.08862828649580479,0.0965808516960602,0.01640943787060678,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",330,4.23956507537514,-0.18357278406620026,-0.32718995213508606,0.8679764047265053,0.08828260341225838,0.475590200163424,0.23976966457614549,0.08369792625308037,0.09442809127254832,0.016619379865005612,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",380,4.876972704194486,-0.19990411400794983,-0.35243141651153564,0.861664678901434,0.09078039849660333,0.46976825408637524,0.22888188398713885,0.08261074125766754,0.09131586016611477,0.016502371057868004,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.004739163909107447,2.8586952686309814,2.8456997871398926,0.9878546893596649,0.0033371405739988738,1.4515181668102741,0.29510366633483404,0.3221462108194828,0.20992421494642055,0.016727101989090443,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,0.26161317666992545,0.48537203669548035,0.45244070887565613,0.9336801543831825,0.04299391474154715,0.585037331096828,0.23346277794569334,0.1347934603691101,0.13240634037854426,0.016479817917570472,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",70,0.9020799403078854,0.13237300515174866,0.018003778532147408,0.8969727568328381,0.0426110021651999,0.4987183241173625,0.2250883198615191,0.10473465919494629,0.11464805866354034,0.016482973005622625,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",120,1.536414185538888,0.05817753076553345,-0.07221643626689911,0.89062824472785,0.050786312060826726,0.4959983825683594,0.2402751022078546,0.09355363249778748,0.10279688694469963,0.016316259978339076,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",170,2.1630108733661473,-0.044014375656843185,-0.17592504620552063,0.8893727958202362,0.04687841763668831,0.5005158158019185,0.2436440601103922,0.09210694767534733,0.09933456036581446,0.01610417291522026,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",180,2.2856482656206936,-0.039202094078063965,-0.17843088507652283,0.8805178441107273,0.06553841075088496,0.49100787565112114,0.23407447989029903,0.09316730499267578,0.09967476264910947,0.016169092152267694,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",220,2.789157882332802,-0.06521410495042801,-0.20801801979541779,0.8746899105608463,0.08103677134062978,0.4849344342947006,0.23562207933630774,0.09167409874498844,0.09724150203662334,0.016279561910778284,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",290,3.659402522724122,-0.1260974258184433,-0.2728623151779175,0.8712752014398575,0.08968072394640628,0.49201174546033144,0.24551392840400085,0.08915327303111553,0.09610292830810403,0.015956734074279666,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",340,4.273277665721253,-0.15840177237987518,-0.3174781799316406,0.863400686532259,0.09786142920702334,0.4809482954442501,0.24098964193834949,0.08475677855312824,0.09154265462887996,0.01564827305264771,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",350,4.395578338764608,-0.16267059743404388,-0.32465025782585144,0.874179657548666,0.06334402777103733,0.47726655565202236,0.24626918282037114,0.08353868871927261,0.09130018526119632,0.015718614915385842,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.0024192791897803545,2.8430373668670654,2.827967643737793,0.9873955100774765,0.003742909314507241,1.474489513784647,0.3207605103026804,0.31309547275304794,0.20626768565486672,0.01599847711622715,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,0.6070315376855433,0.17027486860752106,0.07634935528039932,0.8913556970655918,0.07901307438436346,0.47305101063102484,0.20774797914293577,0.10055350326001644,0.10975687151640902,0.015236488077789545,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",90,1.0836490720976144,0.06618303060531616,-0.03769438713788986,0.8901680931448936,0.0673539243171328,0.4849534658715129,0.22660284306673592,0.09510872140526772,0.10384772697323984,0.01521480712108314,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",120,1.4389528899919242,0.02278394252061844,-0.09554003179073334,0.8888568170368671,0.04549411353657807,0.4813320795074105,0.2336020839927861,0.08743581734597683,0.09570867225257546,0.015131908003240824,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",170,2.0319478646852076,0.010342256166040897,-0.1275642365217209,0.8864463455975056,0.07030318654326755,0.4900826755911112,0.23251199153884589,0.09072019904851913,0.09429447630746064,0.015000151935964823,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",220,2.625843191985041,-0.06676271557807922,-0.21020042896270752,0.8875506184995174,0.06293060705161618,0.4927692739292979,0.24009820992071734,0.08763733878731728,0.09425068579093836,0.015137468930333853,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",270,3.2184907912742347,-0.10455173999071121,-0.2518661618232727,0.8749373890459538,0.09325266794681042,0.4839916806668043,0.2347072708946362,0.08822126127779484,0.09781265577314524,0.015081115067005157,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",280,3.3369731639977545,-0.11416509747505188,-0.2645392119884491,0.8764655031263828,0.08860475296179691,0.4943646127358079,0.24294250638346512,0.09271304868161678,0.09826692940309395,0.015092155197635293,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",320,3.8105437024496496,-0.1310313493013382,-0.2856697738170624,0.8771992959082127,0.08516619658472083,0.4927707863971591,0.245895704454827,0.08794101886451244,0.09471865177896331,0.014944104012101889,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,0.4874505610205233,0.252547949552536,0.18726548552513123,0.9113129861652851,0.04721803219267177,0.507625937461853,0.2225275534214834,0.11346441134810448,0.12767876294760586,0.014906756114214659,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",90,1.087871509604156,0.12178853154182434,0.001970707904547453,0.8944426476955414,0.04860565190044992,0.4974391059949994,0.2361882247054834,0.09803162887692451,0.10853370600833834,0.014903439907357097,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",100,1.2077486785128713,0.1072595864534378,-0.024252215400338173,0.8979218378663063,0.04448022632785309,0.5083071850240231,0.24140254252452192,0.09597843326628208,0.10434457286804895,0.014995120000094175,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",150,1.8046523905359209,0.03955928236246109,-0.10409808903932571,0.8911448903381824,0.04328495542723708,0.4930085688829422,0.23819647197417382,0.09085983783006668,0.09667596488712195,0.014866109937429428,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",190,2.287606590660289,-0.0090659623965621,-0.16116032004356384,0.8893424682319164,0.04633530225644723,0.4912000112235546,0.2450770551580564,0.09009236469864845,0.0990422438031676,0.014909522142261267,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",260,3.1319207868073136,-0.03774787485599518,-0.19627538323402405,0.8881551548838615,0.05011203207937632,0.489373404532671,0.24114757270710016,0.08848650380969048,0.09719213465214716,0.015047685010358691,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",270,3.2533124471083283,-0.054787538945674896,-0.22283640503883362,0.8883326426148415,0.05158223882653318,0.4986716127023101,0.24582802792772201,0.09016695246100426,0.10009248906547732,0.014860986033454537,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",320,3.857022043550387,-0.053941760212183,-0.21939235925674438,0.8709706217050552,0.08943807687233908,0.48674693517386913,0.2405120562365897,0.08425884321331978,0.09262703906263839,0.015275618992745876,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,0.4832112714648247,0.24858973920345306,0.18102481961250305,0.9181794598698616,0.03942675691800336,0.5029667420312762,0.22557976846746536,0.10562789067626,0.11596410586797905,0.0149421407841146,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",90,1.0857974556274712,0.10855366289615631,-0.005826347973197699,0.8979140035808086,0.04296844458189779,0.47576600778847933,0.22053873655110598,0.09225117228925228,0.10437950776939449,0.015008250018581748,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",140,1.6852238469291478,0.04560957849025726,-0.08628559112548828,0.8887413516640663,0.06303692655091434,0.4858705196529627,0.23278935133237647,0.08847229182720184,0.09673332367081125,0.014877199195325375,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",160,1.9247071440331638,0.030028315261006355,-0.11211715638637543,0.8828747235238552,0.07814108793835338,0.4827906070277095,0.22838566354876438,0.08732082694768906,0.09719579790486199,0.014835353009402752,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",210,2.5272251213900745,-0.01947007328271866,-0.16591708362102509,0.8867868594825268,0.0629548416026389,0.49486147426068783,0.24325945306270064,0.08937033824622631,0.09566677288981024,0.014830502914264798,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",260,3.1310063682030886,-0.06312951445579529,-0.210359588265419,0.8812538012862206,0.08847492707739779,0.4793356768786907,0.23452899446537057,0.08615360222756863,0.09343098042219866,0.01491586584597826,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",270,3.25070155528374,-0.06250929087400436,-0.22143806517124176,0.8811949826776981,0.08703000047858625,0.48816619673743844,0.2312644673337857,0.0878679919987917,0.09248727596360003,0.014979813946411014,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",320,3.848902796395123,-0.10078935325145721,-0.2565850019454956,0.8803402110934258,0.06915109764494848,0.4922198513522744,0.23920274670327854,0.08760137297213078,0.09166496532379924,0.01499139005318284,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",360,4.329356228467077,-0.11437159776687622,-0.27895504236221313,0.8748525567352772,0.08153796446192721,0.47318016551434994,0.22849552877619592,0.08586299233138561,0.09143252547885916,0.015661071054637432,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",430,5.167677595745772,-0.16578976809978485,-0.3244592547416687,0.8746615834534168,0.08105435462829555,0.4766570026986301,0.22911703100842903,0.08719327487051487,0.0920033933229124,0.014988557901233435,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",440,5.287928373320028,-0.15974633395671844,-0.31243473291397095,0.8660573810338974,0.09516060095671991,0.47706015687435865,0.23101344821203498,0.0868760421872139,0.0901464653204402,0.01516193593852222,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",490,5.890077029587701,-0.17421451210975647,-0.3365965485572815,0.8735904805362225,0.08754183732052631,0.46711036702618003,0.2321272326687173,0.08330894820392132,0.08917049930387483,0.014958589104935527,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",540,6.487806232413277,-0.19947612285614014,-0.3793943226337433,0.8657516911625862,0.09000867019965762,0.4856194704771042,0.24295957836842946,0.08448178134858608,0.08918365272345867,0.01511565805412829,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,0.4826906437519938,0.20485565066337585,0.13325710594654083,0.912962980568409,0.04055645210911912,0.49443902913480997,0.21715958698833115,0.10372022725641727,0.1131326619300493,0.014940571039915085,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",80,0.9607408689334989,0.059434130787849426,-0.049323417246341705,0.8881837017834187,0.05099181614342915,0.4737626248970628,0.2279006580149017,0.08941097743809223,0.09748818682378702,0.015824144007638097,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",110,1.3148942273110151,0.05851259082555771,-0.08924028277397156,0.8888271488249302,0.05997070270213265,0.4864790765568614,0.2306609137431971,0.09104080870747566,0.0966800710752275,0.015070758992806077,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",160,1.9113219089340419,-0.047003112733364105,-0.18299716711044312,0.8893092200160027,0.04968114019534697,0.49107025284320116,0.2443361579022767,0.09060655906796455,0.09930553646309417,0.014956644037738442,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",210,2.5128875474911183,-0.08870352059602737,-0.23050563037395477,0.8777432329952717,0.07236103887896166,0.48285379633307457,0.23766719512486273,0.0881188865751028,0.09686067283435974,0.01511575118638575,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",220,2.63188503542915,-0.0964636504650116,-0.2437174767255783,0.8740220032632351,0.0791300672808389,0.48267444455996156,0.2405721959727928,0.08706326596438885,0.0960907211512731,0.014932614983990788,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",270,3.2314863393548876,-0.14200088381767273,-0.28464123606681824,0.8824130818247795,0.059271613105250184,0.4797486197203398,0.2394343653427185,0.08914002776145935,0.09964581753883968,0.015023129060864449,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",310,3.7686324273236096,-0.1657477468252182,-0.315700888633728,0.8656582087278366,0.09688979997122468,0.48238768614828587,0.244376961993238,0.08524152263998985,0.09537569751895282,0.014950552955269814,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",380,4.8746191470418125,-0.1990247517824173,-0.3550683259963989,0.8624557219445705,0.09653690737916197,0.4846462928690016,0.24696646060022984,0.08434123732149601,0.09388742230876213,0.016509565990418196,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",390,4.9948640710208565,-0.20944331586360931,-0.36270347237586975,0.8707899302244186,0.07474963675570903,0.47923635179176927,0.24363701454291467,0.08024006336927414,0.09042497122493758,0.015415129018947482,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",440,5.776741632260382,-0.22807981073856354,-0.38552847504615784,0.8638287968933582,0.09278932792701891,0.48116192454472184,0.24595236854882033,0.0864356029778719,0.09464004568836304,0.015590877970680594,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",490,6.4402335996273905,-0.2515549063682556,-0.4127722978591919,0.857819639146328,0.10312558790238413,0.4789422517642379,0.256411539004268,0.08216260001063347,0.09404290411785739,0.015145749086514115,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",540,7.041282827733085,-0.23595353960990906,-0.4185304641723633,0.8543359749019146,0.09616513977528472,0.47135741356760263,0.2444331359839752,0.078443493694067,0.09174964815871714,0.014850849052891135,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",560,7.278458493528888,-0.21516874432563782,-0.3936222493648529,0.8580921776592731,0.09674200468210745,0.4773609251715243,0.24845267907223367,0.08041156828403473,0.09285776797980166,0.01491315197199583,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,0.646782694850117,0.13680660724639893,0.06732177734375,0.9065541289746761,0.05040266834841809,0.45697939908131957,0.2155916101330218,0.08575998246669769,0.11624837751584664,0.04396449588239193,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",70,2.138289967318997,-0.9508223533630371,-1.2459053993225098,0.7194395177066326,0.063675631677468,0.19477350637316704,0.07128083411752903,0.020975083112716675,0.03164389799145451,0.04391837818548083,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",120,3.628206596709788,-1.0547986030578613,-1.4794565439224243,0.6920130103826523,0.07548081283902587,0.16236439417116344,0.05300073334501764,0.016562003642320633,0.03442162313535371,0.04343594401143491,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.00417817197740078,2.9132614135742188,2.9104273319244385,0.9866955578327179,0.0037663142015681135,1.551523618400097,0.32468467610881185,0.31370014138519764,0.2052878317434239,0.04425540892407298,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,1.1921872608363628,-0.6945381760597229,-0.893900454044342,0.7916274741292,0.038212755143946155,0.23801943007856607,0.09143691443566646,0.029299240559339523,0.04050635733962742,0.04356767609715462,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",110,3.2489284111652523,-0.9530848860740662,-1.4381837844848633,0.6949147954583168,0.07966519951443188,0.17881547543220222,0.07764884698964668,0.025660216808319092,0.05901754250366834,0.04330911417491734,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",160,4.7254611232783645,-0.32722753286361694,-0.9442654848098755,0.6916145831346512,0.08336675515704017,0.18677937472239137,0.08561389278099694,0.02206556126475334,0.03273535445308934,0.04306971398182213,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",170,5.02212565485388,-1.000531792640686,-1.699752926826477,0.6578633934259415,0.07318024844193148,0.17229413357563317,0.07562386850776906,0.015953317284584045,0.021414728182580323,0.04422559589147568,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,1.189348382409662,-0.48954328894615173,-0.558729887008667,0.846496507525444,0.04836159111564612,0.3214448094367981,0.1336318230573153,0.04513551667332649,0.04933138131575524,0.04454957484267652,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",90,2.6707548080012202,-0.9557844400405884,-1.186033010482788,0.766445379704237,0.05956165102915409,0.21218069922178984,0.07574366109477539,0.025790374726057053,0.043160792871342536,0.04340849118307233,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.004266215022653341,2.8716094493865967,2.871711492538452,0.988067802041769,0.005219960691671942,1.4994396269321442,0.35759207465102544,0.3270989842712879,0.21777360281699465,0.04375260998494923,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,0.8911785180680454,-0.5088390111923218,-0.5722222328186035,0.8410950973629951,0.04555247653833703,0.29660414764657617,0.1355866390143375,0.04568753205239773,0.05525495424696054,0.04334522411227226,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",80,2.3665865047369152,-0.9414181709289551,-1.2168833017349243,0.7435580529272556,0.0647209394217165,0.1899744807742536,0.06917934980442292,0.020518414676189423,0.0343308096466036,0.043389606988057494,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",130,3.841413229238242,-0.9424594044685364,-1.3693641424179077,0.6850532963871956,0.07717838910786096,0.17048476519994438,0.06625083843921314,0.017787471413612366,0.02384436418227893,0.04349670698866248,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.004301651148125529,2.981987237930298,2.970125436782837,0.9886172562837601,0.0029490269852611895,1.6191267222166061,0.3565471774377941,0.3141449764370918,0.2020867735084769,0.0441526600625366,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.29945510416291654,0.39483562111854553,0.32977715134620667,0.9104944467544556,0.07316275406464168,0.5390580417588353,0.2578591136936113,0.1016022153198719,0.12913861503340252,0.043715758016332984,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",90,2.648906626040116,-1.0160582065582275,-1.408496379852295,0.6960723623633385,0.06205388780357324,0.16233073826879263,0.059986616569022384,0.02020210400223732,0.0331325815554946,0.04330445500090718,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",120,3.5259383032098413,-1.1285934448242188,-1.4954997301101685,0.6869457811117172,0.07427271282329032,0.15375212114304304,0.06209861358726624,0.023487776517868042,0.04773410007430556,0.04308120207861066,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,0.8920721702743322,-0.6241961121559143,-0.7154738903045654,0.8121647052466869,0.05209116800193055,0.2783242124132812,0.1340313234258449,0.03471289947628975,0.03809775451966634,0.0436210751067847,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,1.1862240741029382,-0.7338227033615112,-0.8519750833511353,0.8110862299799919,0.05106375585451053,0.2348354673013091,0.07807485382169113,0.031027261167764664,0.042884639890965685,0.04336875909939408,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",90,2.6580146183259785,-0.9743478298187256,-1.3220667839050293,0.7496344447135925,0.060540031704204175,0.19753145519644022,0.09720065054774875,0.02248234674334526,0.03921877263990869,0.04342474904842675,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,0.5966500705108047,-0.031909506767988205,-0.145161971449852,0.890502043068409,0.05243147860001478,0.3924619392491877,0.1658094581835734,0.06148838996887207,0.07396424009477665,0.04351347405463457,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,1.7903713244013488,-0.846418023109436,-0.9845849871635437,0.7605251520872116,0.058012219946107224,0.21237963112071157,0.07721918023325641,0.027741342782974243,0.035976444570844826,0.0438998460303992,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",90,2.6825276252347976,-1.0749590396881104,-1.3387017250061035,0.6823112629354,0.07354406091204908,0.16488239169120789,0.07701766320580399,0.017958205193281174,0.023872925526302356,0.04357810504734516,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",140,4.15643892926164,-1.002932071685791,-1.4873634576797485,0.6773994378745556,0.09079275855988694,0.13837464014068246,0.05325128049090577,0.021699950098991394,0.0516431835121151,0.04391481098718941,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.004361676052212715,2.9603679180145264,2.9698896408081055,0.988434698432684,0.004945161651484729,1.4653504863381386,0.3385158142916203,0.3370856177061796,0.22400926656155784,0.04417017078958452,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,1.4753303229808807,-0.8049409985542297,-1.0000550746917725,0.7761855982244015,0.03626609576697553,0.2142962273210287,0.06804982394534666,0.031041566282510757,0.041004279410048916,0.0428890420589596,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.004341663792729378,2.838104724884033,2.812915325164795,0.9874272644519806,0.0043677034927575385,1.4361964091658592,0.28876897277108826,0.31160397827625275,0.2096147340545473,0.04359023901633918,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,0.5973079176619649,0.13360603153705597,0.05052700638771057,0.8987749665975571,0.06309050981018181,0.4614543877542019,0.23370046955739357,0.08327225595712662,0.11696094000065838,0.04330728901550174,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",70,2.07141293422319,-0.9632300138473511,-1.2144452333450317,0.7377866841852665,0.058210486094318004,0.18166305497288704,0.08750398578971597,0.0226513110101223,0.031107972366958364,0.043125858064740896,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",120,3.533702448476106,-0.9183139801025391,-1.398097276687622,0.6880478151142597,0.07807306847165918,0.16334376949816942,0.0699415002619516,0.028434690088033676,0.04522978425481279,0.04403165681287646,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",130,3.8295000013895333,-1.1687549352645874,-1.609031319618225,0.6857888028025627,0.07076242299144633,0.15202533430419862,0.06481386144773871,0.01954524591565132,0.03184325700374106,0.043179981876164675,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",180,5.325874278321862,-0.9809268712997437,-1.587001085281372,0.6643648259341717,0.07603876415017109,0.14760358678177,0.06045776328643199,0.01624200865626335,0.022398687730493586,0.04363540397025645,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",220,6.511936882277951,-1.0809812545776367,-1.849401593208313,0.6500830426812172,0.07634766407782796,0.1424020752310753,0.050496566771557096,0.01675049215555191,0.025607646899458752,0.042685338063165545,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,1.7873524883762002,-0.9922400116920471,-1.2925398349761963,0.7208293527364731,0.05759754738318979,0.18194068875163794,0.06461938218139847,0.02382727712392807,0.03065266125245678,0.043572847032919526,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",70,2.085498067550361,-1.075273871421814,-1.3812334537506104,0.7033309191465378,0.07209538183050619,0.15903520118445158,0.04575693668601278,0.019663743674755096,0.03357936714131215,0.04283733293414116,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.5278908105101436,0.22342213988304138,0.18657435476779938,0.8996287882328033,0.08845134615423823,0.48314122576266527,0.2323727867681712,0.06666356883943081,0.10466871447117954,0.08694383595138788,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,3.12273714132607,-1.0877163410186768,-1.312281847000122,0.7185636460781097,0.07104923004256893,0.16184387169778347,0.053961415273095927,0.014612764120101929,0.021549240058981393,0.08691817685030401,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",110,5.684229295235127,-1.1653717756271362,-1.5821043252944946,0.6553117372095585,0.07534336989342778,0.13766051409766078,0.05765413791436078,0.011517949402332306,0.0186638774875325,0.08831477398052812,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.5157547928392887,0.21883392333984375,0.17378993332386017,0.9140641316771507,0.04935080810837252,0.49011184368282557,0.2364701879028941,0.07039528153836727,0.10949877265046556,0.08589782402850688,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,2.0536047883797437,-0.8948025703430176,-1.0667611360549927,0.7516756430268288,0.07067201692674421,0.1852889086585492,0.05937252444827421,0.016424763947725296,0.027425421277020058,0.0863585511688143,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",90,4.618216443341225,-1.1237163543701172,-1.5221912860870361,0.6744986325502396,0.062193981473866275,0.1466901795938611,0.05832863166244911,0.010790519416332245,0.017693419254729403,0.08822452509775758,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.007073721848428249,3.0524630546569824,3.0079963207244873,0.9893800467252731,0.0035350731975012555,1.5572293549776077,0.381878662620118,0.2868988811969757,0.22430912686783155,0.08839808800257742,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.5187257076613605,0.3734471797943115,0.30977770686149597,0.9188479520380497,0.05189428386278298,0.5202214997261763,0.24647633711380876,0.08038785308599472,0.1227304531994262,0.08606759295798838,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,3.105078798253089,-0.907554030418396,-1.0793561935424805,0.7583856396377087,0.05012439075909682,0.21384511096403003,0.09266231041042913,0.018419548869132996,0.02240708123142369,0.08816269296221435,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",90,4.632401026086882,-0.9434671401977539,-1.2767925262451172,0.7194480895996094,0.06369224395141303,0.1714078972581774,0.07316060416052106,0.015439026057720184,0.028572837673191145,0.08761230995878577,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,1.0275256738532335,0.06645341217517853,-0.03520060330629349,0.9004195965826511,0.04976873734302803,0.44072239147499204,0.21001949526933064,0.0668204240500927,0.09606765696443889,0.08757082396186888,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,1.5383649710565805,-0.6447434425354004,-0.7714388370513916,0.8049015291035175,0.0415312193699071,0.2436585812829435,0.08384330495931026,0.026448626071214676,0.03258357804110542,0.08758638519793749,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",80,4.1076436205767095,0.48279210925102234,0.3772459924221039,0.9216606579720974,0.03296970574541552,0.6871310193091631,0.29190609967449715,0.17147435620427132,0.2104918443531883,0.08634306094609201,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,1.5242203082889318,-0.5132978558540344,-0.9235808849334717,0.7842375040054321,0.05538687987792082,0.281388898845762,0.13249178115263763,0.0256664901971817,0.026774459766342577,0.08732565096579492,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",80,4.144753579981625,-1.3691519498825073,-1.724421501159668,0.6364127285778522,0.0745054104854048,0.11896993406116962,0.039675598678155004,0.007474645972251892,0.013580866871553172,0.08925248892046511,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.006728392094373703,3.1939313411712646,3.1935248374938965,0.9877910278737545,0.004759934413142274,1.5890533328056335,0.3390415545932618,0.2850276976823807,0.20400259838795542,0.08599648787640035,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,1.5785922061186284,-0.39724108576774597,-0.5173475742340088,0.8430449701845646,0.06840843015324853,0.2942347568459809,0.12482081790099606,0.033158257603645325,0.04338773466475098,0.0870720271486789,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",80,4.153184924973175,-0.8592602014541626,-0.9693636894226074,0.7767282724380493,0.07016017392314115,0.21825689123943448,0.06878684474737734,0.021076835691928864,0.023901352126062283,0.08627390209585428,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",130,6.671831134008244,-1.4795024394989014,-1.882189393043518,0.6211812682449818,0.06824433846643041,0.12171608675271273,0.05514435115156924,0.0069270022213459015,0.009227927571567296,0.0887572520878166,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",140,7.17961023375392,-1.3548527956008911,-1.7838988304138184,0.6286068744957447,0.07887090708867105,0.12075295159593225,0.04492952407102676,0.008716456592082977,0.012205822354426901,0.08450991800054908,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",180,9.256954349577427,-1.339837908744812,-2.0888991355895996,0.6044928878545761,0.06585840309844988,0.1040807943791151,0.04727410987802921,0.006820328533649445,0.013021836223485864,0.08665083907544613,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,2.5862749707885087,-0.7287076115608215,-0.9837115406990051,0.7798456214368343,0.052515049610700935,0.1822336995974183,0.043304396567163554,0.01830412447452545,0.031217502187844304,0.08504933305084705,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",100,5.166146766161546,-0.6724778413772583,-1.276701807975769,0.7236311733722687,0.0638812281417852,0.1915443241596222,0.07934102395976382,0.014348775148391724,0.019000968184454604,0.08519879402592778,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",110,5.663515097461641,-1.1887102127075195,-1.5965276956558228,0.659781739115715,0.07521426779679607,0.18346528406254947,0.07216764991915746,0.013479918241500854,0.019020986192331845,0.08598976582288742,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",160,8.145118989283219,-1.3220088481903076,-1.8904801607131958,0.6280221678316593,0.07807907691114788,0.14431603206321597,0.05898744262041173,0.011415466666221619,0.02643757054666922,0.0838362390641123,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,1.4851728326175362,-0.6647592186927795,-0.8228946924209595,0.805019598454237,0.058598680723946,0.20555135887116194,0.05817546836238955,0.020967353135347366,0.028507745368403233,0.08355162316001952,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",70,3.5637872517108917,-1.2802749872207642,-1.4626325368881226,0.6926862150430679,0.0695753083902712,0.13967178133316338,0.0376400746541076,0.011832211166620255,0.015373079317490369,0.08414644398726523,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",100,5.031662604538724,-1.6024621725082397,-1.8360295295715332,0.6352831870317459,0.05803757398671612,0.11190248979255557,0.050681556674219884,0.006326865404844284,0.008900094507026378,0.08356029097922146,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.007600506069138646,3.1061761379241943,3.131531238555908,0.9902357906103134,0.004420622769311125,1.4833269640803337,0.3168885812417436,0.29303552582859993,0.22159451612550246,0.08447227790020406,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,2.4633859081659466,-1.1138248443603516,-1.3138803243637085,0.7271723076701164,0.052029638900432024,0.19756119325757027,0.09071542695995986,0.016067922115325928,0.018185703449322497,0.08385744597762823,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",100,4.868284786818549,-0.3978026509284973,-0.6183294057846069,0.8191827945411205,0.05393855334190275,0.323120370041579,0.14451642450838256,0.04217541590332985,0.04993923922527391,0.08076087501831353,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",110,5.344360976014286,-1.483478307723999,-1.5858352184295654,0.7204273454844952,0.05579398106032001,0.1681925836019218,0.08009488907539801,0.01314389705657959,0.017100058590449795,0.08313912386074662,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",70,3.310250374954194,-1.0092145204544067,-1.2849657535552979,0.7173145338892937,0.07588027531463462,0.24809813941828907,0.1116253741826244,0.02298824116587639,0.03877284247546564,0.0803655139170587,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",100,4.7229677371215075,-1.160156488418579,-1.4314203262329102,0.6926527358591557,0.07383459231719036,0.18114850530400872,0.07618257872437877,0.020167749375104904,0.03939670076519224,0.07807386084459722,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",150,7.066402045078576,-0.19560949504375458,-0.3298555612564087,0.8736563101410866,0.04580978146263973,0.3421999951824546,0.1282383949395672,0.060689209029078484,0.0763563855178123,0.08097801101393998,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.013047722168266773,2.885209560394287,2.8690621852874756,0.9869694970548153,0.0035066056734821683,1.4177207574248314,0.27219413625389377,0.2985580898821354,0.21683384975671416,0.16693200403824449,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,1.3977438975125551,-1.092886209487915,-1.2356228828430176,0.720829363912344,0.05782564328644818,0.1777251665480435,0.06630724311763057,0.020682647824287415,0.03678358404603181,0.051222385140135884,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",100,2.791596758645028,-1.3608479499816895,-1.5895494222640991,0.6403423771262169,0.05477488485826558,0.16662174090743065,0.08285711127281009,0.010770071297883987,0.016202664043339195,0.04909800109453499,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",140,3.867053271504119,-1.5284827947616577,-1.7771967649459839,0.6362622268497944,0.058179814445864265,0.14086306374520063,0.07524640340158226,0.006593666970729828,0.010995356650811992,0.047918473137542605,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",170,4.720258352579549,-1.517059087753296,-1.7038671970367432,0.6171486005187035,0.07259533940230248,0.13166495342738926,0.07020031956569243,0.0069669075310230255,0.011052325206667053,0.04922601184807718,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,1.0890932197216898,-0.8789288401603699,-1.0011368989944458,0.7723405547440052,0.04154022249891271,0.1330378814600408,0.029489249399700792,0.019703324884176254,0.04281675665114903,0.04776549688540399,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",90,2.4315884199459106,-1.3779672384262085,-1.5608810186386108,0.6566131673753262,0.05993090916980502,0.10767123382538557,0.033547469228651476,0.008761834353208542,0.017323746117977764,0.04858050006441772,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",140,3.8290315449703485,-1.493206262588501,-1.7018344402313232,0.6144012827426195,0.07606405537149659,0.10165961063466966,0.0406635259384229,0.007427573204040527,0.019411873950660952,0.0534555078484118,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",180,4.898386473767459,-1.514140009880066,-1.7565804719924927,0.6510915197432041,0.07823928211397412,0.10006706020794809,0.03385519895771957,0.006428621709346771,0.014656458019914846,0.05065333307720721,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",210,5.710261288797483,-1.4549856185913086,-1.7012101411819458,0.6584478728473186,0.07050254915350454,0.12247678404673934,0.04916488112979579,0.009977143257856369,0.017316326716989315,0.047715177992358804,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,0.5410249147098511,-0.3670031428337097,-0.46330514550209045,0.8343823403120041,0.04759496256616825,0.33380420599132776,0.14431876730041504,0.04260416328907013,0.05077924500186479,0.04907078901305795,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",70,1.935057686874643,-1.269416332244873,-1.4205244779586792,0.6776071190834045,0.0646659061444732,0.14155459124594927,0.04263415754554897,0.0107123963534832,0.017371560662325324,0.04883298184722662,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",80,2.2476607807911932,-1.3366528749465942,-1.4862719774246216,0.674841970205307,0.055765751417825186,0.1412365180440247,0.04589457459118186,0.01090974360704422,0.016860563236425756,0.052592955995351076,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",130,3.664213668787852,-1.389279842376709,-1.602283000946045,0.6621045507490635,0.06257107685604935,0.1311164628714323,0.0517785368203304,0.010294564068317413,0.019092835095731453,0.050767986103892326,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",170,4.720845996867865,-1.4843698740005493,-1.716355562210083,0.640717163681984,0.06920964958438013,0.12388390209525824,0.04989046399259352,0.009868107736110687,0.024645391618422198,0.047324280021712184,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,1.0659358403645456,-0.7770635485649109,-0.9030324220657349,0.7631763927638531,0.05810823808431851,0.1964304270222783,0.06159229431139104,0.019396845251321793,0.027716411326040753,0.047903989907354116,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,1.3298640728462487,-0.9130796194076538,-1.0630522966384888,0.7569890096783638,0.05045458086303255,0.17113340739160776,0.05738888586434059,0.01579250395298004,0.02769407627622742,0.04771157703362405,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",100,2.657080205855891,-1.3272311687469482,-1.505116581916809,0.6511501893401146,0.08982469568565685,0.14676273334771395,0.05999165459240823,0.019101548939943314,0.047603208635078866,0.047309936955571175,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",150,3.970266695600003,-1.2308712005615234,-1.5132861137390137,0.6534806825220585,0.06387515633381446,0.13941606413573027,0.04403867790346222,0.0161978080868721,0.02800403848762508,0.049376612063497305,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,0.5290268941316754,-0.6661964654922485,-0.7556437849998474,0.7826586216688156,0.05726459802049351,0.2634284971281886,0.10855934741735619,0.03483005613088608,0.05104753682280488,0.04719462292268872,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,1.6317775743082166,-1.2635698318481445,-1.4053232669830322,0.6871617324650288,0.06182234749770046,0.11816912796348333,0.029981721204324338,0.016517169773578644,0.03879780010708558,0.05222693015821278,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",90,2.4486118308268487,-1.2974340915679932,-1.4586987495422363,0.6724554188549519,0.07396778638429645,0.11657125549390912,0.041762812137143224,0.015609566122293472,0.029055048655989287,0.047604291001334786,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",140,3.789112823549658,-1.526563048362732,-1.664242148399353,0.6792650073766708,0.06376403732507326,0.11343439272604883,0.03993553650499841,0.009167104959487915,0.022408471414682603,0.04633505083620548,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",190,5.096068938262761,-1.580462098121643,-1.7322769165039062,0.659538209438324,0.07430746975415435,0.10281377169303596,0.028278616864781603,0.008503858000040054,0.020703014350850767,0.04625972709618509,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",200,5.364827458746731,-1.512045979499817,-1.7130188941955566,0.657506637275219,0.06382239167808045,0.11399412737227976,0.044007999954533625,0.009333979338407516,0.026082258462304404,0.04477067687548697,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",250,6.691132485866547,-1.5921931266784668,-1.7294833660125732,0.6535878665745258,0.08859391375017488,0.12198114837519825,0.049439067910770955,0.010709568858146667,0.027731137847058013,0.044791512889787555,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.273908160161227,0.5328457951545715,0.3773335814476013,0.9160421788692474,0.05531910719725488,0.5070104440674186,0.18449862885574583,0.09095937572419643,0.1123205685754741,0.04673083801753819,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",90,2.381267523393035,-1.328925609588623,-1.518282413482666,0.6474331468343735,0.0567519243713541,0.15797917172312737,0.07501383726092661,0.012347836047410965,0.022627907791281796,0.046057480154559016,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",100,2.6382169651333243,-1.2444249391555786,-1.4385735988616943,0.6913817226886749,0.045121431977654086,0.14568007458001375,0.059415623163001474,0.014001797884702682,0.02248896156426285,0.04572436003945768,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",150,3.9301052300725132,-1.4897865056991577,-1.6674449443817139,0.654088344424963,0.06130705161112322,0.13615597365424037,0.06179743289495783,0.00975547730922699,0.015819162764556943,0.04625671682879329,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.002720603020861745,3.196714162826538,3.0988478660583496,0.988952025771141,0.0039849688482861394,1.5093166381120682,0.32116317146266343,0.3282216228544712,0.21978717121028774,0.04427806497551501,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,1.3326476595830172,-1.1149336099624634,-1.1617929935455322,0.7360813841223717,0.053727085672218525,0.1225733831524849,0.019000283647106927,0.018676243722438812,0.03749120208742871,0.04546834412030876,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",90,2.319593681488186,-1.373981237411499,-1.556623101234436,0.664365865290165,0.0679518134739278,0.12872043065726757,0.048542972163817165,0.014792460948228836,0.036413192425704094,0.04476745310239494,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",120,3.057402821490541,-1.4767224788665771,-1.6421253681182861,0.651979248970747,0.06810826828505685,0.12994881020858884,0.044421554777852666,0.012131892144680023,0.025211685772433868,0.04615829908289015,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",170,4.299884042004123,-1.5357005596160889,-1.7463352680206299,0.6368169635534286,0.08424120883722945,0.11399618536233902,0.04204965310650603,0.013430193066596985,0.04320578773162174,0.045994029147550464,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",220,5.517128266161308,-1.536385178565979,-1.7643747329711914,0.631001178175211,0.07087400985229503,0.11488185590133071,0.03873463233957353,0.012098293751478195,0.03247561509772947,0.043445849092677236,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.0028063328936696053,2.957685708999634,2.903599977493286,0.9878237508237362,0.004314164288961527,1.4289924167096615,0.28976667005667484,0.3045720253139734,0.22370777739959405,0.04353369399905205,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,0.9767120671458542,-0.7742411494255066,-0.9093130826950073,0.772032618522644,0.040924940368646606,0.15114205470308661,0.02925294824104203,0.019006934016942978,0.035281475877677895,0.041001468896865845,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",110,2.68059263844043,-1.2611786127090454,-1.4033806324005127,0.7041936032474041,0.053712030143411305,0.11103980103507638,0.021732813349965508,0.010463729500770569,0.020688185675110486,0.044845358934253454,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",160,3.8950382026378065,-1.3164598941802979,-1.548821210861206,0.6822150237858295,0.05146070439729065,0.12204839056357741,0.03935506925584821,0.010118789970874786,0.017725776256320505,0.04635638580657542,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",170,4.138552410528064,-1.3538254499435425,-1.5703346729278564,0.6719687357544899,0.0659522898680321,0.10315843066200614,0.03772201394754084,0.00893259048461914,0.018773527250986695,0.0458205898758024,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",220,5.354830940486863,-1.382789969444275,-1.5712069272994995,0.6700680553913116,0.07536698619019841,0.10819141170941293,0.040023241852517925,0.009533151984214783,0.02479588115570225,0.043976776069030166,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",270,6.570904739899561,-1.4271963834762573,-1.724783182144165,0.6507300734519958,0.057449391165205636,0.09958020481280982,0.031978776691006884,0.006943996995687485,0.014925042387222814,0.046099756844341755,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,0.7364921488333493,-0.6233513355255127,-0.7266088128089905,0.7947701178491116,0.044658795542887875,0.20761826448142529,0.09149231382393734,0.030682407319545746,0.05448222276788554,0.04439853201620281,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,1.4680209597572684,-0.9035999178886414,-0.9746665358543396,0.7705607824027538,0.05496783061276899,0.19003429589793086,0.08415517507912039,0.02376561239361763,0.04821592789296262,0.04333427408710122,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",110,2.689359058625996,-1.070163369178772,-1.1223143339157104,0.7475902065634727,0.05997990916087955,0.17853899439796805,0.09129379330653846,0.02309150993824005,0.04667259596132738,0.04386187205091119,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",160,3.9096110013779253,-1.0991313457489014,-1.2495545148849487,0.7405648082494736,0.05390217029488834,0.1611555553972721,0.06025715587805228,0.01954500749707222,0.033770684048305764,0.04322505299933255,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",210,5.129830479389057,-1.1164308786392212,-1.345078468322754,0.7105360366404057,0.06172833632347788,0.16331101255491376,0.06335273773543368,0.01711782068014145,0.03031361945416945,0.04277839488349855,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",220,5.374070626683533,-0.8867233395576477,-1.2381714582443237,0.7252178117632866,0.04958801597872363,0.15133319655433297,0.05327944034228779,0.017423372715711594,0.031769245629775025,0.04233710700646043,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.0025702391285449266,3.031571388244629,2.9582877159118652,0.9867560863494873,0.0045263353257475,1.3996055647730827,0.2822538313841822,0.29727371968328953,0.21515465540820275,0.04257493419572711,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,0.7394913106691092,-0.7125791311264038,-0.8045454621315002,0.7979070097208023,0.054292286950040874,0.19394321786239743,0.0530459752641518,0.02742164582014084,0.053152024391292726,0.04505164595320821,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",80,1.9610067983157933,-1.0840229988098145,-1.3247466087341309,0.7022115215659142,0.04131911692460247,0.14382324926555157,0.05168823759248938,0.015048317611217499,0.023751517486185137,0.041536245960742235,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",90,2.206067073624581,-1.1140201091766357,-1.2589552402496338,0.7370612397789955,0.05106257697545552,0.1503968108445406,0.0499930028643334,0.024874258786439896,0.05280289143674481,0.044898551888763905,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",140,3.430993706919253,-1.3553823232650757,-1.6340601444244385,0.6573588885366917,0.07420488646579912,0.14972408628091216,0.055977110370464364,0.014146246016025543,0.0283617507735479,0.04415538581088185,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.017838748870417476,3.34309458732605,3.2405619621276855,0.990143284201622,0.003637815816368045,1.595401655882597,0.3658480843170507,0.352439034730196,0.20453246698253374,0.295045254053548,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,2.212875769706443,-1.4581080675125122,-1.6702346801757812,0.6527403183281422,0.07454055049488359,0.10335453739389777,0.03270830564902629,0.013609468936920166,0.026725580259312896,0.15045974520035088,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",70,5.119470580713823,-1.743274450302124,-2.1047475337982178,0.6140983365476131,0.09312485087086599,0.1387900875415653,0.05930717353339547,0.015378858894109726,0.03381592329363479,0.15552171017043293,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",120,9.10335733415559,-2.0077600479125977,-2.3660316467285156,0.6015080697834492,0.07867054045306765,0.13035264937207103,0.07224347722502343,0.01227748766541481,0.026677992738399672,0.15826583001762629,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.005244469968602061,2.8483121395111084,2.8181965351104736,0.987152423709631,0.0035244093252190752,1.3390866965055466,0.2176985900887109,0.2833411730825901,0.21033390078366673,0.144375093979761,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,3.756338762352243,-0.9089727997779846,-1.0827587842941284,0.758180420845747,0.05882246813016157,0.13725312240421772,0.06288894303022194,0.02087562531232834,0.04459431013890546,0.14697857480496168,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,4.48021881445311,-1.4948625564575195,-1.7717689275741577,0.6172113046050072,0.0823909501862022,0.10866388189606369,0.06275685757647706,0.01006387174129486,0.028264249392236224,0.14820748986676335,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",140,10.456891786772758,-1.9489911794662476,-2.23885178565979,0.5873647108674049,0.07490014102852904,0.0982454523909837,0.057576067693784316,0.009584568440914154,0.026410809648549756,0.14856676012277603,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",170,12.779630719916895,-1.7814445495605469,-2.17814040184021,0.6026895605027676,0.07604315386339477,0.10452513513155282,0.05913030172901723,0.01120464876294136,0.023287526366950084,0.1502650750335306,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,2.91396924899891,-1.3989330530166626,-1.658531665802002,0.6435501538217068,0.0552768604943476,0.14882892835885286,0.07724239482460109,0.010135922580957413,0.014384982462258873,0.1485758109483868,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,3.6378088204655796,-1.4111063480377197,-1.7092697620391846,0.6484282091259956,0.06756902967821424,0.14869927265681326,0.07586266553697225,0.007445637136697769,0.007761864756278271,0.14764026296325028,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.008248341036960483,2.9504036903381348,2.9429142475128174,0.9863586276769638,0.003623249017922701,1.491924099624157,0.2737804781981372,0.3029286190867424,0.19802196932535124,0.15604113321751356,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,2.9179994938895106,-1.4014041423797607,-1.5638744831085205,0.6535032652318478,0.07347243737199231,0.1079048051033169,0.03354353483441073,0.009525418281555176,0.017881657134846008,0.1506847192067653,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",110,8.032690492924303,-1.7924995422363281,-2.036714553833008,0.6154384575784206,0.07707714215566679,0.1087692971341312,0.0513129265659586,0.008911330252885818,0.020545232554084053,0.14885999402031302,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",120,8.759204343426973,-1.7194236516952515,-2.040006160736084,0.6251177750527859,0.061634258178405905,0.11761127202771604,0.05985850974826199,0.006969355046749115,0.011277610761310392,0.14486566185951233,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.00511800404638052,3.2054126262664795,3.1883738040924072,0.9894709102809429,0.007679453363758144,1.8247283771634102,0.6163930481099303,0.38472640700638294,0.21109513648498895,0.14652963797561824,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,3.757336189504713,-1.7388479709625244,-1.997343897819519,0.6142870672047138,0.07201510963048782,0.1332828428130597,0.11036282826656156,0.0097167007625103,0.01812050361201165,0.14771614503115416,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",100,7.501871193060651,-1.7015527486801147,-2.2273330688476562,0.5982015952467918,0.0717629798237819,0.12483982555568218,0.0936217590355514,0.011004310101270676,0.02102598685580546,0.15620439080521464,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.725484173046425,-0.17846325039863586,-0.2917540669441223,0.8752130419015884,0.04192570260328071,0.32644238136708736,0.09106782539730299,0.05480731278657913,0.07347560433966323,0.1526108100079,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,3.0138175354804844,-1.2865164279937744,-1.4676611423492432,0.6976317912340164,0.08388175599513338,0.15297498647123575,0.05557334661271994,0.01608901470899582,0.02619718124330754,0.1485244920477271,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",90,6.827897001057863,-1.627538800239563,-1.9124692678451538,0.6145878583192825,0.09503417197874878,0.10025874455459416,0.0384731978987424,0.010181251913309097,0.023016869380759655,0.14576394809409976,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",140,10.534922442166135,-1.7892111539840698,-2.2078256607055664,0.599083349108696,0.07075796689209717,0.16164864297024906,0.08333484530553183,0.016347438097000122,0.03195645909044084,0.14679334708489478,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",150,11.371766924159601,-1.8537791967391968,-2.3685083389282227,0.5862991958856583,0.07656944201930214,0.1415239917114377,0.0756783691328291,0.01312236487865448,0.025964209069401466,0.1576744259800762,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.7216406613588333,-0.38776499032974243,-0.45606499910354614,0.8187253959476948,0.0681250653777681,0.34066852554678917,0.16116790791639582,0.03743620589375496,0.04072634174909838,0.14690409903414547,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,2.88185557234101,-1.5753347873687744,-1.826869249343872,0.645054716616869,0.05944388822498026,0.10196468885987997,0.029649472094614496,0.008344080299139023,0.01675736444792152,0.14874656009487808,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.7220332860015333,-0.21835017204284668,-0.3962293863296509,0.8446331769227982,0.038043157495469396,0.27402538154274225,0.06680706105397195,0.030650880187749863,0.03614671404015336,0.14411257999017835,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,1.4408491351641715,-0.9526435136795044,-1.1456332206726074,0.7178548984229565,0.07612520176379582,0.180558396037668,0.05730826128769557,0.02315962314605713,0.04333630421079051,0.14794725691899657,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",70,5.167733002221212,-1.619246482849121,-1.929504632949829,0.5984936505556107,0.06763472298272957,0.10588088305667043,0.05715622968107804,0.00724434107542038,0.016264658982484394,0.14912587008439004,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",120,8.90591737232171,-1.83993661403656,-2.1993050575256348,0.5955716706812382,0.09287688226028738,0.08991603995673358,0.04347443926226147,0.010652054101228714,0.032660901061462334,0.14804308488965034,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",170,12.646968544926494,-1.706376075744629,-2.373159408569336,0.5955788269639015,0.07771753806579451,0.10142964660190046,0.05328233281919517,0.010116558521986008,0.026268382337960947,0.14707248588092625,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,2.229762031696737,-1.4221787452697754,-1.5890069007873535,0.6466012448072433,0.07276721322199528,0.15837326576001942,0.07402579063308429,0.013400062918663025,0.02362782079411476,0.14489639387466013,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,4.351337006315589,-1.5866374969482422,-1.8428059816360474,0.6204421781003475,0.07604072794282099,0.12491263193078339,0.06035224958444956,0.009196564555168152,0.015575159316607622,0.14570777886547148,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",110,7.9859415327664465,-1.7791944742202759,-2.1061627864837646,0.6068420112133026,0.07651130019376932,0.11845524841919541,0.05881104709080658,0.01086941733956337,0.024232248538459157,0.14180405205115676,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.7081401632167399,-0.27881529927253723,-0.31778833270072937,0.8684664405882359,0.03463581517534762,0.34115713089704514,0.10306813431696725,0.04420917481184006,0.04779640799145151,0.14505065884441137,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,1.4127452510874718,-1.0769133567810059,-1.181028962135315,0.7293698936700821,0.054176366078242194,0.15852198144420981,0.042283224847230245,0.014962121844291687,0.027522259550632294,0.13871820201165974,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",70,5.0260625325609,-1.7724533081054688,-1.974088191986084,0.6024663057178259,0.09228084615661787,0.09498634375631809,0.04704434017989711,0.012863725423812866,0.03826122554664072,0.14286462287418544,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",100,7.133157697273418,-1.9731684923171997,-2.1917662620544434,0.5841754879802465,0.08862312340006459,0.09552777977660298,0.06669993765401862,0.011173766106367111,0.03294040259982059,0.13905287999659777,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,2.3645704246591777,-1.2442514896392822,-1.4534997940063477,0.6863436289131641,0.08139077014848309,0.17768618604168296,0.0941070168422035,0.030787698924541473,0.06548500434564411,0.21287474292330444,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,3.55201265681535,-1.3607155084609985,-1.5947167873382568,0.7074878551065922,0.07483953404967629,0.15964065538719296,0.08370538206894997,0.015958458185195923,0.0237862706927725,0.21639565099030733,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",80,9.433362918905914,-1.977003812789917,-2.276423454284668,0.6016858480870724,0.061757177145234834,0.14167031948454678,0.06978927678701394,0.01127602532505989,0.020527891872281753,0.21517979609780014,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,1.2109232251532376,-0.7019791007041931,-0.6668559908866882,0.7926599681377411,0.050928031008881255,0.23887972999364138,0.049186230144417725,0.03755461052060127,0.057748758243174214,0.2192128801252693,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,5.893468474736437,-1.7281575202941895,-1.8701238632202148,0.6580428965389729,0.09109615358248511,0.11978060565888882,0.04896536426437654,0.01334170252084732,0.035277027675004964,0.21496236603707075,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",80,9.408344615949318,-1.6901041269302368,-2.075068235397339,0.6110227182507515,0.05705042434864067,0.10157665819860995,0.03301879189329776,0.009868849068880081,0.016975464238336915,0.21384685300290585,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.023613539058715105,3.3411903381347656,3.2467336654663086,0.986052181571722,0.006153148603191578,1.4805383905768394,0.38304688345301807,0.28016881458461285,0.2130674915109715,0.2133078258484602,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,5.988934391411021,-1.5858768224716187,-1.9203033447265625,0.633734717965126,0.07229471713639915,0.1322505329735577,0.08967852964098616,0.013621732592582703,0.02913997936733014,0.21706215501762927,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",100,11.858534581493586,-1.6754047870635986,-2.0883045196533203,0.6083094477653503,0.05997113528173495,0.1232030619867146,0.05928124398098297,0.009880941361188889,0.019266785178706017,0.21624288987368345,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",110,13.0400483624544,-1.704458236694336,-2.126551628112793,0.6371118538081646,0.06255832172959319,0.14133997401222587,0.07365570745604216,0.012366864830255508,0.02231816099431235,0.22144519397988915,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,3.5405149403959513,-1.504827857017517,-1.6954376697540283,0.6426670514047146,0.07638900632150633,0.11254280875436962,0.03817548434648799,0.01663113757967949,0.03547241154668975,0.2182132361922413,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,7.086271831765771,-1.9236335754394531,-2.259096622467041,0.5984750054776669,0.07480912994299814,0.14411086472682655,0.06246939388067027,0.01218029111623764,0.019693666933759284,0.21872339607216418,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",110,12.791865460108966,-1.9475667476654053,-2.418225049972534,0.5764412023127079,0.072222208534419,0.12819799408316612,0.07831043121915879,0.010669451206922531,0.02577034500328259,0.2106124700512737,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",120,13.900147149106488,-1.6889418363571167,-2.2049524784088135,0.6092452555894852,0.08701670351900145,0.12810964230448008,0.06926778931398775,0.011218298226594925,0.028590252352002587,0.20258445199579,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,4.4391281513962895,-1.7461763620376587,-2.0460238456726074,0.6146173737943172,0.06640600270471335,0.10720024676993489,0.04907771984260455,0.006447631865739822,0.010022379074551401,0.20638169790618122,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",90,9.990815029945225,-1.8291090726852417,-2.295973777770996,0.612794890999794,0.07178086531946168,0.11707076756283641,0.08898320085196286,0.008854322135448456,0.01811225483412715,0.2043390399776399,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",120,13.318627891363576,-1.7722669839859009,-2.3241190910339355,0.5922285951673985,0.06095559677133212,0.10798349906690419,0.06778055782165371,0.00859963521361351,0.015907563839861444,0.20506876101717353,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",160,17.762647875817493,-1.9412974119186401,-2.624694347381592,0.5921927392482758,0.07170033374306829,0.11406316421926022,0.09067407265778521,0.008973173797130585,0.01831623921108383,0.20509666111320257,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,4.44865808240138,-1.1821510791778564,-1.4018498659133911,0.6822167374193668,0.09126042479525169,0.123888174071908,0.03631213828571309,0.01884850114583969,0.03290752830184096,0.20685112406499684,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",90,9.993724510306492,-1.7538431882858276,-2.0166823863983154,0.6350757386535406,0.08622930449083382,0.11692734970711172,0.06672573996805314,0.010230250656604767,0.03004414412214237,0.20761836506426334,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,2.2247682691086084,-1.305484414100647,-1.447797417640686,0.7024235911667347,0.0611602126080242,0.15002615097910166,0.0895153486219522,0.01503453403711319,0.027670067050061524,0.20650867489166558,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,3.3346522704232484,-1.4957480430603027,-1.6704847812652588,0.6938368454575539,0.06425192112377735,0.1091184918768704,0.038182092567567806,0.011406712234020233,0.02195162376596256,0.21042220201343298,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",110,12.222601188346744,-1.7702116966247559,-2.116274118423462,0.6293296404182911,0.053008303786868945,0.12669332441873848,0.07360367727901886,0.009791996330022812,0.013794951111695881,0.20453799306415021,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,2.2241968507878482,-1.162677526473999,-1.235518455505371,0.7018396407365799,0.08253615512895009,0.20568773802369833,0.09603671627251094,0.028152648359537125,0.048525770700116024,0.20397364906966686,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",70,7.766401291824877,-1.8964569568634033,-2.1962642669677734,0.6063238643109798,0.06427606702710727,0.11556370882317424,0.06757926308084282,0.008395101875066757,0.016225206835173676,0.21082827798090875,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",80,8.876040491042659,-1.6003034114837646,-2.0619778633117676,0.6238457895815372,0.05875122742820716,0.1242627864703536,0.06657673693982429,0.009859371930360794,0.016716467201784917,0.20629459712654352,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,2.2256708575878292,-1.3440803289413452,-1.4859048128128052,0.6694645658135414,0.06705195496193754,0.13809815794229507,0.061461824370031316,0.01510145515203476,0.026052419093340835,0.2036270061507821,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",70,7.777006021467969,-1.789302110671997,-2.1546719074249268,0.6044624783098698,0.06753529069513785,0.09729092917405069,0.026772362295697582,0.009780019521713257,0.01659063221131539,0.20398874091915786,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",100,11.103800819721073,-2.069981098175049,-2.4083614349365234,0.5806602947413921,0.07110360306437409,0.09340888657607138,0.039854987114367545,0.009435418993234634,0.02285556580924616,0.20729421800933778,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.007542093982920051,2.9710187911987305,2.99066424369812,0.9884657114744186,0.0030323733407040523,1.541529729962349,0.3677392722835296,0.279183954000473,0.22085833845545455,0.20806791610084474,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,5.566111421445385,-1.6182475090026855,-1.899247407913208,0.648745309561491,0.06582616275237806,0.12142919446341693,0.056157232827247244,0.012294631451368332,0.023444485212841485,0.20541425189003348,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,0.3913050168193877,1.143673062324524,1.1314644813537598,0.9566327705979347,0.014547973282782426,1.0092062540352345,0.2637280305038814,0.22835156694054604,0.17505010710910865,0.02983494708314538,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",60,2.179159054066986,0.7558552026748657,0.7059412598609924,0.9375908561050892,0.014042263766994611,0.9576004445552826,0.2592431475269251,0.21968233957886696,0.17089599352742713,0.030543186934664845,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",100,3.0358218180481344,0.5735162496566772,0.5207356810569763,0.9245927035808563,0.020953226737293117,0.9287248626351357,0.2731361342971143,0.22103935666382313,0.1828114841821728,0.029500973876565695,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",130,3.934817361878231,0.555403470993042,0.4353500008583069,0.9219044074416161,0.023084757948616228,0.9238545782864094,0.2816775532573059,0.21894734911620617,0.18074215341284827,0.029795272974297404,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",180,5.420146979158744,0.5385952591896057,0.4298498332500458,0.9240993857383728,0.023883968052112778,0.9352469872683287,0.2771187930611472,0.2313294503837824,0.187383633101429,0.03026161785237491,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,0.9687487629707903,0.8963814973831177,0.9079239964485168,0.9418025128543377,0.011501550472872685,0.9891642406582832,0.2584508588186985,0.22182065062224865,0.17096197565782595,0.030567682115361094,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,1.2745726900175214,0.8702582716941833,0.8170743584632874,0.9399110712110996,0.013911791725394914,1.0195195004343987,0.25662638354225503,0.2253156304359436,0.17183308394954613,0.030806440860033035,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",90,3.0809277379885316,0.6861843466758728,0.6508069038391113,0.932492770254612,0.015364242942208253,0.9294249191880226,0.2785878460205824,0.22582080028951168,0.18276901800476655,0.030695261899381876,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",120,3.5931805290747434,0.6516687870025635,0.5276700854301453,0.9246830493211746,0.01727361400369332,0.9365795738995075,0.2669771727172708,0.22605564072728157,0.18056748359843278,0.030473486054688692,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.07954681199043989,2.684561252593994,2.663839340209961,0.9850762002170086,0.0033707207529924564,1.3795132339000702,0.2404866061042343,0.2945998087525368,0.1921723632968426,0.029019717127084732,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,0.35799035406671464,1.2145366668701172,1.1955437660217285,0.9547426030039787,0.014697692677860804,1.0690755285322666,0.2652614477499162,0.23105936869978905,0.17004597315213743,0.029020338086411357,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",60,1.8706695549190044,0.7964112758636475,0.7253372669219971,0.934905257076025,0.013812942432914237,0.9768706187605858,0.2632017135701071,0.2253882773220539,0.1735361710644039,0.029078923864290118,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",110,3.181585919810459,0.6318842768669128,0.576715350151062,0.9274900890886784,0.018003755717322115,0.9219701755791903,0.2621544549253689,0.21830281615257263,0.17377705908833663,0.029817358125001192,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",160,4.411016315920278,0.44153520464897156,0.36669111251831055,0.9182703979313374,0.026676593296933487,0.965563490986824,0.286737160220651,0.22896216809749603,0.1860285826782137,0.030506422044709325,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",190,5.259785356931388,0.5106903910636902,0.4136344790458679,0.9199829362332821,0.022849382349562023,0.9339953139424324,0.27087071913804944,0.2262681070715189,0.18281516670146372,0.03211293998174369,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,0.9017334270756692,0.9103073477745056,0.8897254467010498,0.9453713782131672,0.014380492987692587,1.0106431134045124,0.2742210694494959,0.22234907187521458,0.16968948592009847,0.030276792123913765,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",80,2.2565858741290867,0.7310312986373901,0.6289023160934448,0.9309697933495045,0.016882390292868095,0.9098079055547714,0.27159905439495535,0.21721387840807438,0.17877485360988254,0.030654882080852985,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",130,3.768931845901534,0.5870842933654785,0.4813400208950043,0.9253185912966728,0.021792611837958047,0.9165194388478994,0.282025695008713,0.22696653008460999,0.18838670925675505,0.029464469058439136,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",140,3.8596054459922016,0.512748658657074,0.41491812467575073,0.9143400005996227,0.023981604527843627,0.9501028005033731,0.2605395901466955,0.22610333375632763,0.17851471949463987,0.029564084950834513,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,0.3524216040968895,1.0668874979019165,1.0752018690109253,0.9494930431246758,0.01834653163852729,1.0020070225000381,0.24580782576121993,0.2210657224059105,0.16716459502841147,0.030245878966525197,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,1.1643018019385636,0.8726826906204224,0.8007420301437378,0.9403712898492813,0.013435653753654825,0.9744965061545372,0.2762032932306334,0.22874221950769424,0.1781076105976851,0.030390841886401176,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",120,3.355652696918696,0.5730848908424377,0.4776155650615692,0.9238315522670746,0.021490706644013462,0.941887691617012,0.2777359538344519,0.22887255810201168,0.18420619153106366,0.029860825976356864,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",130,3.600948505103588,0.6487144231796265,0.5114014148712158,0.9228509478271008,0.02326426322656792,0.9191446248441935,0.273815774300828,0.22943498939275742,0.18451143786301813,0.032231364166364074,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,1.1734012949746102,0.8435253500938416,0.8071566224098206,0.939452338963747,0.01589725082511411,0.9657493382692337,0.2711126998826073,0.22519792057573795,0.17613717552103794,0.02991702384315431,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",90,2.5299964151345193,0.7029708027839661,0.6244689226150513,0.9296267591416836,0.015312009650180355,0.9441371448338032,0.27634896099534795,0.22753814980387688,0.1851231028236855,0.030238872161135077,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",140,3.968494534958154,0.5765228271484375,0.4647764265537262,0.92032091319561,0.021185534871244,0.9360057767480612,0.2560055458113364,0.22557787224650383,0.18204736454391038,0.030227721203118563,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",170,4.7574279280379415,0.439353346824646,0.31622231006622314,0.9216920398175716,0.021391504478875875,0.9782107453793287,0.3215633625317103,0.23364396765828133,0.18810096913709104,0.02935441001318395,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.08539032307453454,2.8780934810638428,2.8691020011901855,0.987640455365181,0.003999017969817158,1.5816277340054512,0.29878597185273353,0.3082979377359152,0.19411883007431466,0.030194565886631608,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,1.4230111190117896,0.8651899099349976,0.8039485216140747,0.9414367340505123,0.01066944522269175,0.9445256590843201,0.26603909002218434,0.22537600249052048,0.17687379315344925,0.030151102924719453,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",100,2.762722978135571,0.645908534526825,0.5844125151634216,0.9296281039714813,0.019120671799246106,0.9304607603698969,0.26765804144761446,0.23087803460657597,0.18840906007415434,0.030173287028446794,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",110,3.1615465928334743,0.564880907535553,0.49439457058906555,0.9258997291326523,0.016958773709640697,0.9225160740315914,0.2571825146861188,0.2249945942312479,0.17893031375023635,0.030288340989500284,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",190,5.040343210101128,0.49610090255737305,0.3842567503452301,0.9204682111740112,0.025177233693927832,0.9241814650595188,0.2855904269972388,0.2210796494036913,0.17831877517507141,0.02904919208958745,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,0.6097852559760213,0.9646192193031311,0.9147371053695679,0.9448539018630981,0.012039891041748782,0.9917083941400051,0.2685721068202935,0.2283050436526537,0.17570193462656924,0.028637917945161462,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",70,1.8991739870980382,0.720946192741394,0.6723797917366028,0.9375595562160015,0.015639100592214433,0.9432540461421013,0.27123178333643416,0.22873837314546108,0.18266555521505967,0.029165523126721382,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",80,2.1648175260052085,0.7352385520935059,0.6452115774154663,0.9353665150702,0.014439028175485204,0.9419625699520111,0.2655914270511729,0.2228383868932724,0.1769649777148063,0.02852871106006205,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",130,3.561012136982754,0.5561913847923279,0.4453955590724945,0.9236153848469257,0.0197399589173189,0.9295393638312817,0.28187683108993034,0.22554327733814716,0.18159537291071134,0.031114290934056044,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,1.1165865191724151,0.8157945275306702,0.7667422890663147,0.9400654435157776,0.012928852705675568,0.9547771438956261,0.2553192529583941,0.22337137535214424,0.17741799551551982,0.02752062608487904,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",70,1.8876237550284714,0.7826129794120789,0.7142423391342163,0.9369496442377567,0.014767970011481417,0.9583818502724171,0.27574949224024486,0.2229517288506031,0.1739677120544789,0.027517243986949325,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",110,2.907733597094193,0.6583491563796997,0.5353189706802368,0.9271239340305328,0.02137133723199413,0.936992647126317,0.26708054865634584,0.23023150488734245,0.1849162620045794,0.027310267090797424,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",160,4.328223529038951,0.5974513292312622,0.45200401544570923,0.9208936169743538,0.023087086364987484,0.9151544459164143,0.27631328557857665,0.22608506493270397,0.18603955151069623,0.030357873998582363,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.08206798485480249,2.873863458633423,2.8543217182159424,0.9859922528266907,0.003783923653850237,1.5027899369597435,0.25629716341231934,0.30176040902733803,0.1942111242431595,0.02740338398143649,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,1.3720440808683634,0.8047067523002625,0.7822108864784241,0.9383232407271862,0.014296612670718474,0.9619907513260841,0.26164384587473694,0.22250981256365776,0.17648309149143304,0.027224116027355194,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",60,1.6320312621537596,0.7680171728134155,0.6798144578933716,0.9335637129843235,0.012456288888852099,0.9553257338702679,0.26768806556277674,0.22216968797147274,0.17432204529795572,0.02723686513490975,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",130,3.5441957460716367,0.5504069328308105,0.4484595060348511,0.9192490689456463,0.023137113320841365,0.9486160930246115,0.2799729152044218,0.2243915069848299,0.182822248218715,0.03017918113619089,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",170,4.431428199168295,0.5055553913116455,0.40467095375061035,0.9153118133544922,0.026815619257950698,0.9402228333055973,0.2690583987115767,0.2245250754058361,0.17975555398816626,0.026429554913192987,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",220,5.848603449994698,0.4112531542778015,0.30966463685035706,0.9163223803043365,0.023236742850121724,0.9683327190577984,0.2916744538616451,0.23011192865669727,0.18158944538873034,0.026434161933138967,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",230,5.993578841909766,0.46034058928489685,0.29508543014526367,0.9191022887825966,0.023434756760217213,0.9453725051134825,0.28203966223239874,0.22897027432918549,0.18331767552256326,0.026751177152618766,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,1.2647159260232002,-0.3786894679069519,-0.3797973096370697,0.8527134172618389,0.04986169561984516,0.3156088823452592,0.11825968130864124,0.05975857190787792,0.060367535358466944,0.08943597204051912,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",70,3.961422231979668,-1.3133540153503418,-1.4032478332519531,0.6901583336293697,0.07263384386762635,0.19815079774707556,0.12556530117432865,0.02369658648967743,0.03329697892328524,0.09327966393902898,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",100,5.960089091211557,-1.0328174829483032,-1.3496344089508057,0.7103589214384556,0.06525545489219041,0.1751699303276837,0.06362244289249099,0.02051958441734314,0.029241357901053013,0.08375117694959044,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,1.765935747884214,-0.6920313835144043,-0.8297871351242065,0.7803909741342068,0.06913191242316513,0.23969003232195973,0.09606211486483274,0.03426315262913704,0.04120869900697328,0.08063438697718084,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",80,4.865009090863168,-0.6388823390007019,-1.0834128856658936,0.7424280643463135,0.05535435784375596,0.1866378174163401,0.057535718836554554,0.027622994035482407,0.03699852400107124,0.08541154395788908,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.14659550203941762,3.100109338760376,3.0910274982452393,0.9884326159954071,0.0029580742959581935,1.6743629425764084,0.19116306582776993,0.3200359009206295,0.19528902602264492,0.08304966008290648,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,2.8741752540227026,-1.0829505920410156,-1.2597298622131348,0.718844223767519,0.05811145309273342,0.24167687632143497,0.10888370579259975,0.030902080237865448,0.031040619465994887,0.08242877898737788,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",60,3.4030859421472996,-0.6063682436943054,-1.0917608737945557,0.7392278872430325,0.053914554923634554,0.16972443042322993,0.056851274467340764,0.02132442593574524,0.0281204655769083,0.0836725439876318,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",130,6.969748591072857,-1.3576538562774658,-1.6503297090530396,0.6631447933614254,0.0759582330680311,0.31091097369790077,0.17062866452411568,0.03937511146068573,0.04660458095632377,0.08131950604729354,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,1.653292089002207,-0.7386594414710999,-0.803570568561554,0.8036226853728294,0.04259829678401281,0.29030428174883127,0.1156971236779757,0.04122544452548027,0.03965999381387153,0.07785303494893014,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",80,4.114970733877271,-1.1245205402374268,-1.315798044204712,0.7264983803033829,0.0596257527590614,0.19379948312416673,0.08480548757894615,0.023354142904281616,0.025596885534075322,0.07878713682293892,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",90,4.598371483152732,-1.0846402645111084,-1.5539989471435547,0.669063001871109,0.07250966015771591,0.17172970972023904,0.10055271337833109,0.01587478443980217,0.017923331815838627,0.07708004303276539,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",140,7.270981158129871,-1.4401443004608154,-1.7696446180343628,0.6578944064676762,0.08716513927378869,0.15641728416085243,0.05869920734235535,0.02462315931916237,0.043196294543443085,0.07632065704092383,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.1326075359247625,3.064028739929199,3.046877145767212,0.9882808476686478,0.004066726128433886,1.6570435985922813,0.25343469137036967,0.3211080823093653,0.20203858157031748,0.07747900602407753,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",80,4.315892037935555,-0.9951311945915222,-1.390391230583191,0.69771833345294,0.07045174964095811,0.28534176480025053,0.1583687650571672,0.033280231058597565,0.027359625077797444,0.08914869092404842,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",90,4.644612069008872,-1.0526820421218872,-1.4987139701843262,0.7005585469305515,0.06966287338998103,0.31285181222483516,0.18390808201261863,0.036173734813928604,0.03294702126251684,0.07704186905175447,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",140,7.124505799962208,-1.3175292015075684,-1.6237140893936157,0.669368851929903,0.06950991522536591,0.11925514554604888,0.037989594096915616,0.016654621809720993,0.028811183962103148,0.08721079910174012,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,0.6447533471509814,0.3783181309700012,0.3874334394931793,0.9196760356426239,0.04208194506054326,0.5746918860822916,0.17929393256562332,0.16065423749387264,0.1582062607167408,0.07637723907828331,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",60,3.1140780930873007,-0.846326470375061,-1.0801221132278442,0.7508166842162609,0.06385037305479513,0.27273222524672747,0.10839708082943011,0.041082341223955154,0.04880684206855829,0.07499281200580299,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",90,4.79636193998158,-1.3542500734329224,-1.585426688194275,0.6759925372898579,0.06080974853237426,0.1696248915977776,0.09213555026999473,0.01989658921957016,0.026199288430101286,0.07501317211426795,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.13236080901697278,3.0407280921936035,3.041839838027954,0.9879769384860992,0.003135901753459877,1.653458133339882,0.21911367028929388,0.31517475470900536,0.19681994553004736,0.07981781917624176,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,2.69079547515139,-1.1359952688217163,-1.2691975831985474,0.7380611561238766,0.058090414201402485,0.16885917726904154,0.06442688093297237,0.020633824169635773,0.023415918655085903,0.07927070907317102,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,0.6472488620784134,0.10825174301862717,0.07200275361537933,0.8885772116482258,0.06635947486216752,0.4401256665587425,0.16795022908621407,0.1007436215877533,0.10789319829618253,0.07875794800929725,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,1.299216371960938,-0.27111366391181946,-0.33005353808403015,0.8574542254209518,0.06768243844532279,0.3487072391435504,0.13916397353324045,0.06889518350362778,0.0747224521174436,0.07852804008871317,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",70,3.657552510034293,-1.3209919929504395,-1.5796772241592407,0.6786179915070534,0.06763989374942114,0.22096285922452807,0.0901636895934485,0.028693262487649918,0.03679636349939604,0.07763502583838999,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",100,5.095023398986086,-1.199892520904541,-1.4513018131256104,0.689968466758728,0.07466007529318182,0.14237980172038078,0.061524615507692774,0.021366186439990997,0.03875276325697407,0.08590699313208461,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",70,3.6111504279542714,-0.8436068892478943,-1.1170310974121094,0.741363525390625,0.054105603277889915,0.18955222284421325,0.06507558255939588,0.022427931427955627,0.027374366381537323,0.07727068709209561,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",80,4.091259324923158,-1.0007398128509521,-1.3130992650985718,0.7118814513087273,0.06433507956488566,0.1982045858167112,0.09683851521869881,0.026985764503479004,0.03585134695805404,0.0777972920332104,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,2.143906557932496,-0.8324640989303589,-1.137150526046753,0.7396843880414963,0.0524800621881723,0.2798937982879579,0.12243653313324768,0.03280992805957794,0.02855565100870065,0.07742067100480199,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",90,4.684469328029081,-0.9491355419158936,-1.4688658714294434,0.6936012133955956,0.06727088978367136,0.15573561238124967,0.06464444573131228,0.01690705493092537,0.02320644525886627,0.08513470110483468,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,3.6665931071620435,-1.247556209564209,-1.4038221836090088,0.7005615271627903,0.06443686993879388,0.2540452848188579,0.10184539121136878,0.02908199280500412,0.026293550256623343,0.15530822589062154,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",70,6.042919461848214,-1.4451113939285278,-1.6197758913040161,0.6533618606626987,0.08093988118053144,0.13249987829476595,0.07863757167634634,0.01293167844414711,0.01800094153297212,0.15136327082291245,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.20271642715670168,3.258028507232666,3.3299293518066406,0.9882805496454239,0.0037296318766264686,1.6918893977999687,0.09513877070600012,0.3276281822472811,0.1875456945699946,0.15116068487986922,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,4.477897268021479,-0.9287782907485962,-1.1696267127990723,0.7192997485399246,0.06822340343263873,0.1826162552461028,0.11981353857536331,0.027246013283729553,0.04730917659834269,0.1536168190650642,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",100,8.191071778070182,-1.3991258144378662,-1.5246185064315796,0.6891481317579746,0.05763776089553697,0.16006413521245122,0.11535892355791622,0.015913523733615875,0.01648705901489747,0.1538435211405158,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.21125276386737823,3.4702041149139404,3.4665560722351074,0.990081861615181,0.00241546821065426,2.180541045963764,0.2279866299576603,0.3516337499022484,0.16646804262093395,0.15251710219308734,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,4.479510365054011,-1.0851041078567505,-1.3570940494537354,0.6910425461828709,0.0570809604116476,0.1921326178126037,0.06752608182627308,0.020054485648870468,0.018684168607416334,0.15419038687832654,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",120,9.886871943017468,-1.3364174365997314,-1.635284662246704,0.6528937332332134,0.08478265969244217,0.15655481861904263,0.05723116357659442,0.020225007086992264,0.04020282714337712,0.14931320096366107,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,1.783556423150003,-0.33261212706565857,-0.4957622289657593,0.8205019645392895,0.051850008713682595,0.27028727950528264,0.09799773383821407,0.04587378725409508,0.035975580699249346,0.15015961998142302,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,2.5392564279027283,-0.7714577317237854,-0.9410227537155151,0.764187041670084,0.07717879840140016,0.20388617692515254,0.0775962496433173,0.025616608560085297,0.024504369918916562,0.1481834719888866,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",80,6.1967134170699865,-1.2584365606307983,-1.4767158031463623,0.6861297562718391,0.05016194655937338,0.15201859967783093,0.04532501460096371,0.018852688372135162,0.019606013910338446,0.1434931370895356,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,1.6835306969005615,-0.5254954099655151,-0.5950224995613098,0.8354157209396362,0.04283573501882998,0.2814607988111675,0.10144355474838346,0.05249708890914917,0.052936052990112004,0.14269946701824665,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,3.993360845837742,-1.1886208057403564,-1.3658109903335571,0.6939681842923164,0.061326504971345036,0.19564833026379347,0.07774072231049835,0.02206459641456604,0.018758734935666373,0.1441523740068078,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",90,6.942498730961233,-1.2154054641723633,-1.3899732828140259,0.7203419208526611,0.07018316825630432,0.14076282130554318,0.05632329062742689,0.018464159220457077,0.028983999615951644,0.1442105348687619,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,1.68286565807648,-0.5439367294311523,-0.5751431584358215,0.836030475795269,0.039515151295730466,0.2869175490923226,0.14454478519214545,0.05228158086538315,0.050253063218777295,0.1432660729624331,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",70,5.4625971869099885,-1.2322763204574585,-1.4770987033843994,0.6701703295111656,0.06641454218840143,0.17418609699234366,0.07009835552676737,0.018038969486951828,0.016557127827217775,0.1443740229588002,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",120,9.286650796886533,-0.5352708101272583,-1.6298185586929321,0.6717841029167175,0.04780441527707143,0.13083372311666608,0.06371113699142728,0.009368807077407837,0.011427791359216968,0.14283378701657057,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",130,10.041556198848411,-1.0264720916748047,-1.5802842378616333,0.6863699480891228,0.052292809080747196,0.15994363138452172,0.06851855465379676,0.01746198534965515,0.02990601593821366,0.1421788448933512,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",60,4.710508515825495,-1.3168116807937622,-1.5628197193145752,0.6684298440814018,0.07159433667996738,0.2404533615335822,0.1163398244205368,0.02921133115887642,0.02692530834558719,0.1422145830001682,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",100,7.704427853925154,-1.2121421098709106,-1.5534474849700928,0.6637867540121078,0.05131722228374836,0.1572473612613976,0.0893518955720098,0.017615824937820435,0.028362943627361158,0.1413589760195464,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",150,11.715623129159212,-1.5408490896224976,-2.12930965423584,0.6206962913274765,0.08320410880592743,0.15545585309155285,0.0660887913195886,0.018322177231311798,0.03153922362582763,0.13961578207090497,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",160,12.267888527130708,-1.4945179224014282,-1.9041446447372437,0.6275561973452568,0.05882955709139224,0.11693903594277799,0.06971348849512421,0.007425315678119659,0.009047922695080443,0.14175256714224815,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,2.5311883660033345,-0.790756106376648,-0.8586769104003906,0.7724968753755093,0.07358818253155723,0.19702409487217665,0.06562480580099847,0.03180348500609398,0.036547234235685525,0.1411143180448562,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",80,6.350941145094112,-1.6646411418914795,-1.9050215482711792,0.6350684314966202,0.07812611964750892,0.1103655791375786,0.04653094741132281,0.012155409902334213,0.020574743515592384,0.141878894995898,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",110,8.537468005903065,-1.0654348134994507,-1.7348248958587646,0.6449280604720116,0.07993186404606589,0.16311538638547063,0.053834319175999304,0.02126932144165039,0.03593800410552484,0.13986216415651143,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,2.5657350241672248,-0.5450633764266968,-0.7386954426765442,0.7840221561491489,0.05352194875383944,0.2271634517237544,0.07829140422151959,0.030843984335660934,0.030973560539129082,0.1493777649011463,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",80,6.067602185998112,-1.056480884552002,-1.4821940660476685,0.6837075874209404,0.07368454960819835,0.19640410458669066,0.08015642125235319,0.02193646878004074,0.02982301092205015,0.14297269796952605,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.18827928486280143,4.255833625793457,4.259490966796875,0.9940482527017593,0.0016492031555155366,3.278261214494705,0.2847531313008013,0.4271130710840225,0.10947629173788496,0.15528159821406007,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,4.017624719068408,-0.9697302579879761,-1.1354864835739136,0.7330011427402496,0.05726997828960104,0.16276322724297643,0.05832273748996569,0.02049707993865013,0.02076523114946373,0.147801412967965,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",60,4.7319367858581245,-0.9106495976448059,-1.4185497760772705,0.6984444446861744,0.07482088439367056,0.16578450752422214,0.07960231437674736,0.019628476351499557,0.024690953657863137,0.14302562503144145,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",130,9.934778939932585,-1.0499155521392822,-1.4856153726577759,0.6743412017822266,0.08090419302262673,0.13187078619375825,0.05457442792991204,0.023822370916604996,0.06453215284850772,0.1423710398375988,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,0.8941245160531253,1.0197147130966187,1.0162118673324585,0.9454072341322899,0.014647311848218391,0.9876110143959522,0.27899669103206937,0.23569948598742485,0.19269352103352191,0.16168554709292948,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",60,3.22271342901513,0.7499796152114868,0.578903079032898,0.9279142320156097,0.013692579074852418,0.883188508450985,0.28572740510537686,0.21415378339588642,0.181570230461547,0.10937901702709496,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",70,3.995569558115676,0.5797867178916931,0.5194782018661499,0.9190043397247791,0.020491417833135107,0.8526519853621721,0.2792345421029138,0.21082364954054356,0.18107350032698108,0.1017807989846915,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,2.617673061788082,0.8475606441497803,0.7421236038208008,0.9353614524006844,0.010443106970338116,0.9426368493586779,0.3062924387389769,0.22189317643642426,0.18546704597681704,0.24460122897289693,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",80,3.796936993021518,0.5191372632980347,0.36299264430999756,0.9172339588403702,0.017952689893577078,0.9330480117350817,0.24670021368137446,0.21311970055103302,0.17030084618446667,0.10450637992471457,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",100,4.726506070932373,0.500311017036438,0.3505071699619293,0.9082347676157951,0.020978073960383144,0.915344500914216,0.27860155683446197,0.21000582166016102,0.17561026312030653,0.10606409702450037,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",150,7.5799042959697545,0.41447338461875916,0.2239019125699997,0.9100317507982254,0.02466309156308051,0.9693355932831764,0.3533755262824197,0.209962734952569,0.1738678668352973,0.10327183897607028,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,1.4578526820987463,0.8009277582168579,0.7064510583877563,0.9346006773412228,0.014140037461994933,0.8901168312877417,0.27042771250204983,0.217850586399436,0.18420182581407984,0.09964996413327754,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",80,3.5209898720495403,0.7300013899803162,0.575061023235321,0.9241034016013145,0.015249081784495097,0.8762210942804813,0.25376796342408636,0.2045277114957571,0.1696197824702194,0.09662363305687904,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",130,5.6036761668510735,0.5427878499031067,0.4213877022266388,0.9164719954133034,0.021703841682848744,0.9513764753937721,0.33557916928254594,0.2141829039901495,0.17178884065706646,0.09758595190942287,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,0.9649436778854579,0.9113287925720215,0.8736406564712524,0.9407981298863888,0.012699811541157392,0.9313974864780903,0.2588104993006445,0.22629845701158047,0.1848465487549077,0.09763897908851504,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",60,2.643430150114,0.90977942943573,0.9057252407073975,0.9343590065836906,0.015574779959701508,0.9123377986252308,0.2735630924559003,0.21524992771446705,0.1804155436876718,0.09414353687316179,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",110,4.630936616100371,0.5556110739707947,0.37376585602760315,0.9071065671741962,0.020090355290777316,0.9324793815612793,0.27716875074939334,0.20728430710732937,0.17021807465449174,0.0860698011238128,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",160,6.633405425818637,0.3558359742164612,0.11151495575904846,0.9079576209187508,0.02219915515746568,0.9425875209271908,0.3144022507226244,0.2043410874903202,0.165746940731381,0.08732851198874414,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",170,7.21049405192025,0.340451180934906,0.12859925627708435,0.9099121205508709,0.020552704852937684,1.0793070159852505,0.46674074737685084,0.20685182511806488,0.16317599239779176,0.08637231891043484,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",220,9.454508754890412,0.5463864803314209,0.40572911500930786,0.9262400157749653,0.017132344360146046,0.914515383541584,0.29496256159181405,0.2139227520674467,0.17783711673105074,0.11056795693002641,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",60,2.749288900056854,0.5686735510826111,0.4035365879535675,0.921357050538063,0.016175784727920514,0.9133387580513954,0.2670986223136614,0.21290132217109203,0.1789246533526612,0.0888771798927337,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",100,4.212257529841736,0.6709156632423401,0.4738004803657532,0.9201644323766232,0.013588615463438364,0.8965805266052485,0.2729813236891037,0.2074666414409876,0.17174632191257977,0.08775117993354797,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",110,4.7602455609012395,0.441257506608963,0.26381078362464905,0.9128748066723347,0.0176859225834212,0.8872122801840305,0.26295223249866995,0.20852357894182205,0.17189888760684485,0.08814165089279413,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,0.5248575629666448,1.241539716720581,1.14469313621521,0.9504396542906761,0.009919377862597221,0.9525224082171917,0.2678572987197926,0.22795509919524193,0.19056472038679545,0.08724450087174773,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",60,2.5805832559708506,0.7052159905433655,0.5859067440032959,0.9239195175468922,0.0179114971648108,0.8927426990121603,0.26378330681138723,0.21334527619183064,0.1815845167732674,0.08658941090106964,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.10557421785779297,2.4662208557128906,2.4922821521759033,0.9826049655675888,0.004560664089182193,1.2838468104600906,0.283153697988111,0.2851714417338371,0.21698132762212788,0.08562354906462133,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,1.3453564320225269,0.8028891682624817,0.7352455854415894,0.936340194195509,0.012553874799075361,0.9002951830625534,0.27567334065747434,0.22021109238266945,0.18556783632298596,0.08685536589473486,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",70,2.97641275706701,0.5462343692779541,0.4671984016895294,0.9179072938859463,0.02219851865982987,0.8721995465457439,0.27045668180797455,0.21057568490505219,0.17944157881086395,0.08603288000449538,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",120,5.219485684065148,0.41418030858039856,0.38478031754493713,0.9116805642843246,0.019763778258121804,0.885921411216259,0.28003878461025483,0.20344421826303005,0.17018240078484642,0.08722019614651799,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",170,7.04200060502626,0.4490572512149811,0.25061869621276855,0.9078364782035351,0.018311096579046015,0.9134284630417824,0.27240101771927816,0.20610403828322887,0.1677014066607609,0.08581425389274955,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.106037518940866,2.3895351886749268,2.390907049179077,0.983520720154047,0.004695388088603246,1.240362472832203,0.22163290058320284,0.26925811544060707,0.20711738497318252,0.08566003502346575,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,2.146816223161295,0.6815592050552368,0.5495375394821167,0.9294436164200306,0.016109952281074147,0.8931858688592911,0.2844152805725899,0.2187294438481331,0.18576134642881215,0.08595730387605727,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,1.0661369061563164,0.9743000864982605,0.8809307813644409,0.9428094252943993,0.012034659260579577,0.9545926600694656,0.28348107368463143,0.23565569333732128,0.19612963785366738,0.08528033597394824,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",60,2.548131847055629,0.6368695497512817,0.49554917216300964,0.9215703085064888,0.01942689267184764,0.8673183135688305,0.27985483896418445,0.21012918278574944,0.17717499427780578,0.08524159295484424,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",70,2.950828599045053,0.5581756830215454,0.46089011430740356,0.9207179211080074,0.015085567498819763,0.8899412900209427,0.28255967993171094,0.20843422040343285,0.1742758220175056,0.08534884615801275,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",120,5.006128214998171,0.506110668182373,0.2700369656085968,0.9120790585875511,0.01944760377081029,0.9223281722515821,0.2725764654147501,0.21246742829680443,0.17630607167246998,0.08537535998038948,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",170,7.198328045895323,0.3909822702407837,0.23784784972667694,0.9138751551508904,0.023050327541953908,1.0149497352540493,0.3896672780404728,0.20943178236484528,0.1737884674501743,0.08633425808511674,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",220,8.883741415804252,0.3442452549934387,0.08447760343551636,0.9080183692276478,0.02146674407709851,1.0404066890478134,0.3585136237473433,0.2132856249809265,0.17077510255267542,0.08327618497423828,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.10302698402665555,2.2977986335754395,2.3125505447387695,0.9832759201526642,0.005667699690521042,1.209789626300335,0.245209301512058,0.2767775095999241,0.21347187170350723,0.08262255298905075,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,2.081343192141503,0.8847655057907104,0.7638649344444275,0.9363121725618839,0.01798307649370089,0.8825331293046474,0.29165692054949743,0.21881428733468056,0.18412086287288565,0.08148165792226791,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",100,4.041265400126576,0.6186410784721375,0.45334264636039734,0.9184870943427086,0.017746117546396115,0.8699361737817526,0.27604853144103764,0.205576466396451,0.17313991812822258,0.08097349992021918,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",150,6.135749086970463,0.3581397235393524,0.1972436010837555,0.914032593369484,0.023189255365437006,0.9956564381718636,0.4000568440350889,0.20905439741909504,0.17247837241955774,0.08052056701853871,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",160,6.423886639997363,0.4475192427635193,0.3685477674007416,0.910797905176878,0.01993121009120095,0.9013964850455523,0.29256489970016264,0.20495957508683205,0.16878443748383942,0.0880500900093466,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,5.363422998925671,-0.9986006617546082,-1.1326181888580322,0.713684257119894,0.057847147763975204,0.19652823824435472,0.06848327033740133,0.027594927698373795,0.05076767930352184,0.29484741389751434,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",90,9.3820950970985,-0.7886431217193604,-1.0286228656768799,0.7125497162342072,0.07936077547721253,0.1532860780134797,0.052912854128546474,0.022020302712917328,0.028395948264058606,0.2978531268890947,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",140,14.977470017969608,-1.468449592590332,-1.6849751472473145,0.6593958362936974,0.07436293824635595,0.12185671390034258,0.052974078384003476,0.010835852473974228,0.0161221182924519,0.2934232600964606,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",150,15.61734169605188,-1.3072538375854492,-1.7135181427001953,0.6313482448458672,0.061115323500165165,0.12597190728411078,0.05399448165333221,0.009076520800590515,0.014742529677323788,0.3063491890206933,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,2.2963689430616796,-0.6982676386833191,-0.7611013054847717,0.7893101908266544,0.05732144860777679,0.24925093818455935,0.0848590100177118,0.03557977452874184,0.03180529761675977,0.3043754368554801,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",70,7.813438606215641,-1.1544651985168457,-1.3957493305206299,0.7269909493625164,0.043643687150975975,0.15610850788652897,0.048384087137916816,0.016601424664258957,0.01590763454738758,0.3057397077791393,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",90,9.369284005835652,-0.9723570346832275,-1.2451345920562744,0.7255648784339428,0.0654918600636671,0.1633797585964203,0.063608650483975,0.01868252083659172,0.017638870055224857,0.2917848180513829,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",140,14.752693460090086,-1.4433131217956543,-1.6110401153564453,0.6632393784821033,0.07837314139750366,0.11451823636889458,0.03463826863691067,0.016230542212724686,0.027287061376559053,0.29199040005914867,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",190,19.24015822308138,-1.0849120616912842,-1.3337788581848145,0.6982661485671997,0.052862854313246985,0.14474152214825153,0.10642190870694178,0.022809460759162903,0.036507638715181714,0.28884854004718363,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,4.317232287954539,-0.5878412127494812,-0.8852944374084473,0.7610535807907581,0.05664273696905106,0.18649663915857673,0.055662653753570865,0.02512354776263237,0.027769430544509307,0.2892312300391495,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",90,9.263832725118846,-1.031745433807373,-1.3924663066864014,0.7057459354400635,0.07432249976029434,0.14819734450429678,0.052120881538351774,0.014430530369281769,0.023370394918232767,0.2896302391309291,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",100,10.01279737195,-1.4104348421096802,-1.7003164291381836,0.6400458440184593,0.06973567430067844,0.10904325125738978,0.03604725713626041,0.008572105318307877,0.014621832507370841,0.28000319097191095,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,1.1979879820719361,0.03972496837377548,-0.11090505868196487,0.8560753501951694,0.041735748248419184,0.3591422289609909,0.11058976004054218,0.06507572531700134,0.06712509718596905,0.2894834871403873,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,5.052516011055559,-0.9582054018974304,-1.2454299926757812,0.6983682736754417,0.06199635686863703,0.16220345627516508,0.05128121063136019,0.015318237245082855,0.017550144886888358,0.28665073099546134,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",100,10.315396779216826,-1.044173002243042,-1.3742749691009521,0.6946732848882675,0.058969498721001025,0.13158862944692373,0.03121923770331536,0.011642839759588242,0.016508193299244384,0.28903784113936126,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",110,11.105742593994364,-0.8801507353782654,-1.1747381687164307,0.7258649915456772,0.07823271104933786,0.15920196380466223,0.06268627309671762,0.024186786264181137,0.04072723290292939,0.2787649529054761,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,3.1300616420339793,-0.693135142326355,-0.7813675999641418,0.7621721625328064,0.05672798194957662,0.22675101971253753,0.08108092752570979,0.03665965050458908,0.044345262876108266,0.2843976609874517,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",80,8.01593862217851,-1.2014589309692383,-1.453824520111084,0.669741116464138,0.06599910281478959,0.11530146328732371,0.029830611667616785,0.012867249548435211,0.017528972746600147,0.28382212691940367,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",100,10.120196803007275,-0.9120315909385681,-1.3858791589736938,0.7072406634688377,0.06799815196961084,0.12241187412291765,0.021319238769814532,0.014508754014968872,0.0248140866530881,0.28432720294222236,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.23321445006877184,1.8498916625976562,1.8663270473480225,0.9753111377358437,0.007469833078627084,0.9941814206540585,0.14441353492119477,0.24928519129753113,0.17949636308931258,0.28525986219756305,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,5.042675496079028,-0.9376120567321777,-1.1602718830108643,0.7224729433655739,0.06073849083274241,0.15511876111850142,0.04044814130315242,0.021646127104759216,0.02912492270592192,0.2847242059651762,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",100,10.15151090407744,-1.6025347709655762,-1.8527510166168213,0.6228847857564688,0.08006593344435865,0.11346844374202192,0.05682016987435291,0.009875182062387466,0.020284013277528854,0.2854657219722867,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,1.199814184103161,-0.08944627642631531,-0.1300402134656906,0.8565653897821903,0.04312387404148619,0.4137440314516425,0.16014924217074128,0.06703515350818634,0.05595036120354995,0.27982209296897054,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,2.1571823889389634,-0.4737274646759033,-0.5604250431060791,0.8023058958351612,0.05166179038836122,0.24992956081405282,0.08735251677416936,0.035996757447719574,0.03158343266853594,0.2778123819734901,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",80,7.981866319896653,-0.9226866960525513,-1.1803739070892334,0.7438033707439899,0.0574287507654626,0.16729464568197727,0.05318163017038948,0.017442282289266586,0.02296002746348206,0.2782126839738339,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,4.053065482992679,-1.039937973022461,-1.2160240411758423,0.7378850057721138,0.06813263820606068,0.15506800264120102,0.04492708415369465,0.027384720742702484,0.05396428853405336,0.2780370288528502,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",90,8.8993758962024,-1.4540596008300781,-1.6160365343093872,0.673459280282259,0.06846462273647685,0.13692707614973187,0.06671325585025477,0.010666705667972565,0.018169959394833523,0.2756271481048316,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",100,9.853995297802612,-1.237859845161438,-1.4605684280395508,0.7010753490030766,0.07211810727720631,0.1246720221824944,0.04215571809996169,0.018455971032381058,0.04165760811504366,0.27756493794731796,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,2.142600544029847,-0.4000285565853119,-0.47349822521209717,0.787052147090435,0.05823423904460189,0.23344130534678698,0.05545931700437366,0.040885236114263535,0.03831965364795645,0.2786205359734595,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",90,9.00459965900518,-1.4351414442062378,-1.616379737854004,0.6438608430325985,0.07117700796235964,0.10621645301580429,0.051478767385765234,0.008464209735393524,0.01631634156228505,0.27783865411765873,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.2295228790026158,1.8795815706253052,1.8646821975708008,0.9751598201692104,0.009349293086026039,0.9747459255158901,0.09962839160100473,0.23165667802095413,0.16834232688388537,0.28751307306811213,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,1.1760517300572246,0.15755334496498108,0.04548129811882973,0.884007316082716,0.06216978056557851,0.3947854805737734,0.13491564371133513,0.07152370736002922,0.07380497509569597,0.2797957530710846,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",60,5.752719573909417,-1.3933658599853516,-1.5960843563079834,0.6844765618443489,0.08289233499222319,0.1829980155453086,0.08974982350922898,0.020398806780576706,0.03213706448818767,0.269213646883145,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",110,10.59044160717167,-1.3894588947296143,-1.641984224319458,0.6967757493257523,0.05116518693494251,0.12433580495417118,0.09070878006266705,0.009482230991125107,0.013327054980825431,0.2721977820619941,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,9.938715883996338,-1.0101511478424072,-1.2278000116348267,0.7125259377062321,0.06306060053492761,0.14400181733071804,0.030245138320165465,0.020915303379297256,0.03262549089170714,1.1078114390838891,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",60,10.823044803924859,-1.3578341007232666,-1.6531343460083008,0.6664464138448238,0.07217837340837098,0.2294139345176518,0.10649446261820197,0.020903799682855606,0.022511941259294017,0.564867754932493,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.38368762098252773,1.5919568538665771,1.6099172830581665,0.9665540456771851,0.01205319100853323,0.8248280845582485,0.06449025603057683,0.24738059379160404,0.1416098125328044,0.5488417819142342,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,8.577684389892966,-1.3712693452835083,-1.5491771697998047,0.6480452045798302,0.07817493294675519,0.1461454986128956,0.07446784408143642,0.014171484857797623,0.015191031913128436,0.5582672781310976,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",100,17.080741924000904,-1.380761981010437,-1.7156749963760376,0.6722404472529888,0.06517087584714042,0.12295262282714248,0.03962996256579096,0.01718534529209137,0.02707834134648864,0.5568634520750493,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",110,18.496463089017197,-1.315143346786499,-1.6220735311508179,0.6744410768151283,0.06373160192370263,0.13926540315151215,0.07535548128565556,0.011669028550386429,0.010998529763679935,0.5568948509171605,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,7.078868455952033,-0.8531175255775452,-1.0774273872375488,0.7394386902451515,0.05015507116739527,0.1765943872742355,0.06878228845820304,0.023057106882333755,0.02151893685286255,0.5630997389089316,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",110,19.621526875998825,-1.5884166955947876,-1.953165054321289,0.6160545535385609,0.08138761990148274,0.10399278812110424,0.04840839674573269,0.018016278743743896,0.03280762664572616,0.5751945259980857,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,2.04239913308993,-0.1823078989982605,-0.2793385684490204,0.8245837576687336,0.045155443910587865,0.37318601179867983,0.15256103449041147,0.07899449020624161,0.04672171169124083,0.5606778098735958,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,3.7179373051039875,-0.7335901260375977,-0.8863346576690674,0.7137151211500168,0.059810926082195376,0.18888698006048799,0.08707344107313024,0.021380923688411713,0.01926037504213597,0.5681389200035483,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",70,12.351491681067273,-1.4169731140136719,-1.7025951147079468,0.6655227690935135,0.07124054036091841,0.1235640118829906,0.06636135263146349,0.01617717370390892,0.033626513239352014,0.650843074079603,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",120,21.294759682845324,-1.3352404832839966,-1.5520871877670288,0.629582118242979,0.06848117388868347,0.1393555011600256,0.06736096901760949,0.014447387307882309,0.014706767202791715,0.5805725657846779,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,3.655810174997896,-0.6301794052124023,-0.6539173722267151,0.7304046973586082,0.06886727837040676,0.19210239266976714,0.04277809757097263,0.03207078576087952,0.04650013490497174,0.5764037109911442,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,7.0823413447942585,-1.0078120231628418,-1.2306807041168213,0.7214336805045605,0.06127056024072069,0.14110548980534077,0.04596972578854333,0.017245598137378693,0.019298444996531747,0.572527859127149,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.37509713019244373,1.6359734535217285,1.600020408630371,0.9626144915819168,0.011194581113871229,0.784470934420824,0.0887869366425395,0.23577477224171162,0.14003048034643822,0.5559555341023952,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,8.817840909818187,-1.131401538848877,-1.2098356485366821,0.7603178471326828,0.06910248566865494,0.13730756705626845,0.0640281627899565,0.015518520027399063,0.013681869996248028,0.6454583220183849,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",100,17.351094294106588,-1.1643991470336914,-1.497706651687622,0.6860064789652824,0.0787305101603083,0.1356264059431851,0.05986107996763884,0.03186837583780289,0.06453553816670682,0.638740757945925,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",110,18.900893865851685,-1.3610843420028687,-1.7577979564666748,0.6500573232769966,0.0634652133432942,0.10896569257602096,0.030170660257941205,0.019000377506017685,0.0309363736288838,0.6003265460021794,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,5.185230836039409,-1.1564663648605347,-1.3464066982269287,0.685430783778429,0.06748550268785551,0.13878052355721593,0.03989800721453635,0.013846676796674728,0.010839173307345913,0.5694651119410992,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",90,15.728441338054836,-1.3640631437301636,-1.5145611763000488,0.7036141082644463,0.08807850239559913,0.12420424213632941,0.04516069956466403,0.014558665454387665,0.01923626622675262,0.5951900070067495,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.37209511804394424,1.6465880870819092,1.609818696975708,0.971159789711237,0.011657359570010727,0.8469715602695942,0.07933128538496952,0.2607899662107229,0.1528355484940596,0.5497603290714324,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,2.1081915909890085,-0.22787806391716003,-0.3773175776004791,0.823211207985878,0.0503106642094706,0.2684230851009488,0.06307711983066967,0.054792314767837524,0.060915150026281846,0.5575653021223843,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",60,10.436925135087222,-1.2776060104370117,-1.5018248558044434,0.7061181366443634,0.052546879276426435,0.1325326911173761,0.02870761015632007,0.01913858950138092,0.0290463455490082,0.6273396499454975,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",110,18.901099110022187,-1.6984416246414185,-1.908385992050171,0.6401026397943497,0.06846850393904945,0.10465056099928916,0.06585976397711786,0.012006159871816635,0.020742504112552396,0.6187930589076132,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",130,22.448212519986555,-1.1923433542251587,-1.5581845045089722,0.6569582223892212,0.06367729978105209,0.14007752109318972,0.05665592244956012,0.01428581029176712,0.012097269169077017,0.6263892818242311,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,5.107862433884293,-0.49352753162384033,-0.6306459903717041,0.8013870865106583,0.06990699219884614,0.27143772412091494,0.10001793428746424,0.04211314395070076,0.05369476559100668,0.581916730850935,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",80,13.523527218028903,-1.426971435546875,-1.6426481008529663,0.6616843566298485,0.052961231877859956,0.14967570453882217,0.1015090708342278,0.015895746648311615,0.021145513541458633,0.6146441330201924,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,6.766862520948052,-0.8196764588356018,-1.046439528465271,0.7433193698525429,0.04421232002873602,0.14187916228547692,0.035652636343997644,0.023485038429498672,0.032776168636257076,0.548248945036903,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",90,14.6213109921664,-1.3301738500595093,-1.6641011238098145,0.6599313504993916,0.06780933835922463,0.13870582124218345,0.05802606984260072,0.017926238477230072,0.02035725388702235,0.6286532969679683,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",100,16.665098543046042,-1.1434440612792969,-1.4661662578582764,0.7237259224057198,0.06600111235540544,0.14468088326975703,0.05627550175769407,0.0208946131169796,0.024506687450832648,0.6333945551887155,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,0.6592020730022341,-0.9518163800239563,-1.0183148384094238,0.6998739466071129,0.07969167214045576,0.17717125010676682,0.10142701643277849,0.020285341888666153,0.028669083753705554,15.037206344073638,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",90,1.1749967248179018,-0.9892548322677612,-1.061008095741272,0.6820157691836357,0.09374208585727317,0.15446022409014404,0.07118839669652907,0.014482937753200531,0.019795917120998003,14.560685768956318,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.0014246241189539433,2.362403154373169,2.360560417175293,0.9838572852313519,0.002835389982868939,1.4316548444330692,0.32089310838079077,0.27481765300035477,0.2013353098948427,14.285065791103989,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.13046178710646927,0.9240981340408325,0.903390109539032,0.9271234795451164,0.030967091657256893,0.4620018061250448,0.0994869219158237,0.0978201162070036,0.11206140971960334,14.244800832122564,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,0.7749238424003124,-0.43753281235694885,-0.5059428215026855,0.7168467901647091,0.08849717844458059,0.15112585620954633,0.05920060348969793,0.018297608941793442,0.026026603383414327,14.35586918098852,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.0017878860235214233,2.523869752883911,2.522664785385132,0.9838566817343235,0.004234774861237729,1.4271917678415775,0.323399620073144,0.2740687392652035,0.20419886306530594,14.31905296095647,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,0.25858427840285003,-0.009768109768629074,-0.011320829391479492,0.8449137546122074,0.03891160994527223,0.32539431750774384,0.12156888122993466,0.047757647931575775,0.058050975135878174,14.10047061019577,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",70,0.9042045671958476,-1.040883183479309,-1.09141206741333,0.685940433293581,0.10061968367248648,0.20718269515782595,0.12410982578711936,0.0350944846868515,0.05008351293026596,14.06663062889129,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",120,1.5462854797951877,-1.874352216720581,-2.06209135055542,0.6825862415134907,0.06283663055951823,0.3578044353052974,0.22536853610088997,0.05312193185091019,0.07815568950690477,14.363971318118274,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.129796726629138,1.0869903564453125,1.084264874458313,0.9191876389086246,0.030789379667318834,0.4838613048195839,0.15158304552750018,0.09382570162415504,0.10773631091961061,14.166774454060942,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,0.7737962431274354,-0.4238532483577728,-0.5079110264778137,0.6660329401493073,0.08244235720702212,0.1435522367246449,0.07199738588168617,0.015091899782419205,0.02341727990839953,13.932199546135962,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",70,0.9017112615983933,-1.000592589378357,-1.0940475463867188,0.6733391918241978,0.08349212428019832,0.19086936092935503,0.10902991293429176,0.019673720002174377,0.029492157634547322,14.057421369012445,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",130,1.6790803342591971,-0.658277153968811,-0.8210395574569702,0.6544688139110804,0.11165771956145175,0.1339713460765779,0.08742182745092626,0.011567223817110062,0.014022243085548364,13.958339703967795,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,0.38845774019137025,-0.18653139472007751,-0.21464803814888,0.7716364376246929,0.05954587304614147,0.20844532176852226,0.10016344666427454,0.023891601711511612,0.030459971279070516,14.07297230605036,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",80,1.0247784391976893,-1.1735044717788696,-1.2288732528686523,0.660309873521328,0.08090192078585613,0.24507142882794142,0.11193743833136768,0.021341606974601746,0.019918880384117012,13.640372100984678,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",90,1.1528749417047948,-1.1694481372833252,-1.2976045608520508,0.6512084789574146,0.07880759130165267,0.18514626356773078,0.10039977615749916,0.023516841232776642,0.03196664628370115,13.717964412877336,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,0.37981576193124056,-0.7575691938400269,-0.7779548764228821,0.8157655410468578,0.048505143248346004,0.39217031421139836,0.23704860650659584,0.0517205148935318,0.044585734869451915,13.77478407882154,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",80,1.0143584890756756,-1.235472321510315,-1.332958459854126,0.6851206868886948,0.08066698013292521,0.12252697860822082,0.04065278435122024,0.010951574891805649,0.01874995324195161,13.698387968121096,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.12720317905768752,1.0843923091888428,1.079985499382019,0.9298409968614578,0.02142015384786987,0.47421679086983204,0.11842702495023945,0.10072454623878002,0.1153440294086412,13.63775104121305,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,0.251757291611284,0.5604851841926575,0.4893585443496704,0.8359326794743538,0.05767435147799427,0.37509926315397024,0.16313000719164014,0.05557458475232124,0.0625496703129608,13.636806347873062,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",70,0.8777692061848938,-0.7149439454078674,-0.7804208993911743,0.6877944432199001,0.09515613042629995,0.13858423149213195,0.06413204345734932,0.016006268560886383,0.03328692425247207,13.54100234201178,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,0.3784133102744818,-0.08398023247718811,-0.10709898173809052,0.7989802621304989,0.08415004896428092,0.3784299078397453,0.22367317430575173,0.05368541553616524,0.041949399200603414,13.609168370021507,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.0019115579780191183,2.461272716522217,2.4602861404418945,0.9836423732340336,0.003521694111315119,1.4346738383173943,0.33589051754493976,0.2757504917681217,0.20497536262964383,13.943994929082692,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,0.2517816119361669,-0.2605569660663605,-0.316341757774353,0.8125337734818459,0.05041147248574327,0.3000161503441632,0.17724326668303778,0.03639184683561325,0.03806429752907343,13.587499872082844,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",70,0.8777505396865308,-1.0593653917312622,-1.121592402458191,0.710682213306427,0.07823473044357115,0.15723931719549,0.09777588134865,0.022172771394252777,0.03917187621101713,13.53800596203655,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.0019409430678933859,2.35323166847229,2.3500285148620605,0.983643040060997,0.004144650369278344,1.4312012270092964,0.3246380018209267,0.2738047372549772,0.20423846088944203,13.38088263105601,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,0.6138442670926452,-1.2643921375274658,-1.316049575805664,0.7151754759252071,0.0736705142351524,0.20938999950885773,0.1533591534932687,0.023192480206489563,0.028980072773466037,13.147235566983,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,0.7372000920586288,-1.6334010362625122,-1.6363580226898193,0.7336699366569519,0.0653368280894784,0.2138113398104906,0.14996832034202773,0.027346204966306686,0.03709820119291613,13.031635518884286,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",110,1.345702697057277,-1.6375762224197388,-1.7064757347106934,0.7297001853585243,0.07800157235800072,0.3160731503739953,0.20916863890995513,0.047058865427970886,0.049482487500468894,12.93611499806866,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.0017645321786403656,2.470581531524658,2.47052001953125,0.9837651476264,0.004169298157924302,1.4485946670174599,0.3237449322776406,0.31979887187480927,0.18786376984455153,30.118309393059462,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,0.8747040447779,-0.35290420055389404,-0.47412991523742676,0.7278448343276978,0.05242480563132596,0.21031262539327145,0.12546244639625279,0.02008645236492157,0.022042010489265754,30.925894104875624,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.0018791381735354662,2.581981897354126,2.581847906112671,0.9843141473829746,0.005018335990768857,1.4359620995819569,0.3226353960000249,0.3182123005390167,0.19216548919711043,30.24307966697961,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,1.0725670768879354,-1.1263056993484497,-1.2564730644226074,0.7751374505460262,0.04571880390925544,0.2288121224846691,0.10298800818843057,0.023251771926879883,0.022657186933243034,30.0299790201243,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",100,2.1358243010472506,-0.23665551841259003,-0.3971191644668579,0.7025081887841225,0.05915605322462242,0.16646659350953996,0.09169156106859823,0.014567781239748001,0.01350207335850563,29.908596767112613,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,0.7667824865784496,-0.9911067485809326,-1.0350077152252197,0.7802644036710262,0.04431738196707346,0.5016429461538792,0.2605360855491673,0.06894689425826073,0.039618511382301866,30.081720852060243,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,1.1977372833061963,-0.6664525270462036,-0.7775263786315918,0.7412634864449501,0.06400465418803085,0.26513672806322575,0.1333549393928446,0.028386082500219345,0.03795820149871348,29.974707090994343,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.001861613942310214,2.539386749267578,2.539412260055542,0.9852615855634212,0.0038635358130379637,1.440223228186369,0.3186275319605015,0.31697752326726913,0.19191909537558607,30.140966003993526,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,1.0535333754960448,-0.8088657259941101,-0.8086382746696472,0.7645008638501167,0.05521860504842686,0.2411242825910449,0.12127486437179923,0.031012777239084244,0.0368914561786864,29.793776244157925,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",100,2.2880411364603788,-1.6258983612060547,-1.669054388999939,0.7123678401112556,0.0755917711450224,0.17859708657488227,0.08041168061496137,0.023229524493217468,0.03444417537091306,30.24190948298201,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",110,2.497589256381616,-1.2528282403945923,-1.3871757984161377,0.7182620503008366,0.06608018111683742,0.24482081201858819,0.13412119964778124,0.02616453915834427,0.028762220642960294,30.06964928819798,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,0.6279346290975809,-1.2876904010772705,-1.2888364791870117,0.7556524723768234,0.06731057012368023,0.15863178158178926,0.06028177334253205,0.02556828036904335,0.05075696139942014,29.51105880807154,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,0.41781361517496407,0.04958942160010338,-0.043776318430900574,0.82339196652174,0.02485787627275728,0.33248895313590765,0.1639655558755662,0.04046725481748581,0.034271224181936344,28.558727079071105,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,0.41283054184168577,-0.08496716618537903,-0.09099131077528,0.7993379943072796,0.035812198549181835,0.23666158178821206,0.09527750009204704,0.028042759746313095,0.030352566206289337,29.417420245939866,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,0.6176253720186651,-0.5407565236091614,-0.6224151253700256,0.7679143100976944,0.031760924545585205,0.3110194564796984,0.16399754522758306,0.03092741221189499,0.022187992887294065,29.24642905406654,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",80,1.6496005828958005,-1.8271124362945557,-1.921170949935913,0.6810503713786602,0.05756821111162218,0.39123315596953034,0.21538849619278924,0.0487169548869133,0.02452342799939842,29.462043304927647,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,0.8272795146331191,-0.8461098074913025,-0.8936096429824829,0.7665665820240974,0.06398738171372285,0.47517776349559426,0.2678374747266949,0.06444604322314262,0.03807179055746001,28.379051333060488,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",90,1.8496130106505007,-1.6542998552322388,-1.8205758333206177,0.6759264841675758,0.0798927288420649,0.27161026606336236,0.13135668098962114,0.03727148473262787,0.04344709521352796,28.92249473091215,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.001914503052830696,2.5330071449279785,2.533189535140991,0.9833360835909843,0.003200834908189263,1.441971492022276,0.32857419638074076,0.31843632459640503,0.189512238360123,28.78670407505706,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=1024]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,1.042476863367483,-1.3476455211639404,-1.4062745571136475,0.7369833439588547,0.05524828082729277,0.16299000242725015,0.06290099586573693,0.015893161296844482,0.023704928214086315,28.191185251111165,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",10,0.5152817938942462,0.2725878655910492,0.26089411973953247,0.9165635295212269,0.05744192765987544,0.44660255312919617,0.1733331337041807,0.04489219933748245,0.08183828837731907,1.6822096889372915,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",60,3.0863577101845294,-1.1664090156555176,-1.145613193511963,0.6952151246368885,0.06229739583314925,0.0954720308072865,0.01749163023273934,0.006156947463750839,0.01769095079472126,2.653411637991667,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",70,3.612501739989966,-1.275712251663208,-1.2010393142700195,0.679931640625,0.06210205474993248,0.12129394896328449,0.032812237286126464,0.003148511052131653,0.003741205402666632,2.5509633291512728,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",130,6.726934844860807,-1.3695610761642456,-1.3508644104003906,0.6459950692951679,0.05785495748146165,0.13149801082909107,0.047365708116960656,0.006088558584451675,0.009080570603556801,2.823658105917275,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",180,9.308941124007106,-1.3840032815933228,-1.3487768173217773,0.6671697087585926,0.07354633949274018,0.10641265148296952,0.04213722388614913,0.003596533089876175,0.008496101572620407,2.8869697239715606,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",40,2.059610484400764,-1.1184583902359009,-1.08786141872406,0.7249394655227661,0.06670344833371145,0.14122037403285503,0.042411938574046244,0.009135112166404724,0.011899066971392215,2.1792436020914465,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",50,2.5854462005663663,-1.1606924533843994,-1.122572660446167,0.698704157024622,0.050796756169641924,0.11969704926013947,0.042369493547712134,0.00308426097035408,0.005218448148743648,2.340301261981949,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",100,5.166034395573661,-1.2686538696289062,-1.248457670211792,0.6995517015457153,0.05162406666760644,0.11189195653423667,0.046713844977197515,0.005670558661222458,0.01367286665588447,2.543062677839771,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",30,1.5483573011588305,-0.6949154734611511,-0.6941291689872742,0.8023029118776321,0.05567618219376237,0.17152884928509593,0.028254319604546514,0.009541824460029602,0.016148486886747245,1.946074364008382,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",50,2.5819350264500827,-1.1387935876846313,-1.0888351202011108,0.711822859942913,0.06293764585036248,0.11679249675944448,0.028523277347856753,0.002364605665206909,0.003545072873001413,2.255256257019937,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",100,5.134577540215105,-1.3779466152191162,-1.3419464826583862,0.6585743762552738,0.06234822538163001,0.09322905400767922,0.0407122143155879,0.0018352307379245758,0.004358783104898323,2.737186263082549,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",0,0.0018908991478383541,2.9081454277038574,2.8216171264648438,0.9846198447048664,0.006639193393100508,1.3190838322043419,0.31613018586721786,0.22182389348745346,0.18518051610868566,1.2484196089208126,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",50,2.5659947632811964,-1.0465123653411865,-1.0481979846954346,0.7237786240875721,0.0620950692493572,0.13163531245663762,0.049042577232771174,0.0030318982899188995,0.003944905938523575,2.3871565328445286,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",100,5.110898201586679,-1.3228310346603394,-1.2922046184539795,0.672517366707325,0.06510058732998285,0.14537274651229382,0.10178640459633155,0.003218989819288254,0.004333981180401748,2.892989139072597,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",110,5.623689254047349,-1.302994728088379,-1.2506771087646484,0.6901762187480927,0.06385883457873355,0.12050474714487791,0.04078527249421821,0.003643214702606201,0.00521397760420747,2.8022455261088908,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",50,2.497709595132619,-1.2329293489456177,-1.1844284534454346,0.7019025422632694,0.06498712958844913,0.12084508314728737,0.042647599824974396,0.00424957275390625,0.008587887898757073,2.118402169086039,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",100,5.038233001017943,-1.393811821937561,-1.3082060813903809,0.6219431683421135,0.07320841689426195,0.09194740583188832,0.02291282280213447,0.0030875690281391144,0.00697141911305593,2.5948895949404687,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",10,0.5112423645332456,0.2797628343105316,0.2822822332382202,0.9173545017838478,0.04650849442235956,0.4595020627602935,0.16866291780439602,0.033658113330602646,0.06206731954213279,1.6721770430449396,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",20,1.0206234736833721,-0.4643042981624603,-0.4562087059020996,0.8179363682866096,0.07970343835475506,0.30077722389250994,0.1472437000553755,0.02052142843604088,0.023992831377102654,1.9058682569302619,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",70,3.5297442071605474,-1.2371834516525269,-1.2386943101882935,0.6628846488893032,0.07637182298595921,0.12985093938186765,0.0726936170995381,0.002707667648792267,0.003300461935630717,2.5979721390176564,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",0,0.0013465271331369877,2.9604759216308594,2.8595337867736816,0.9845583513379097,0.006660356195129467,1.3236226849257946,0.34675751924354725,0.2351295556873083,0.1970077837605901,1.0685258109588176,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",60,3.016785065177828,-1.2041902542114258,-1.1775654554367065,0.6950686536729336,0.06470570101067116,0.12102335505187511,0.03304335746070657,0.004601966589689255,0.009177645311882298,2.3629555380903184,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",70,3.525409846333787,-1.203935146331787,-1.206795573234558,0.6924721263349056,0.04592861097825021,0.1427834164351225,0.09843543766982066,0.004528634250164032,0.005772026583391798,2.3516240299213678,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",120,6.065570587757975,-1.2815183401107788,-1.2491825819015503,0.7055011503398418,0.052252444782355155,0.10668703867122531,0.034716478124333185,0.008248727768659592,0.0151133547967755,2.5465463721193373,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",170,8.576725675025955,-1.385772943496704,-1.2877507209777832,0.6238062791526318,0.06386707033518337,0.12597150984220207,0.08585129636465329,0.003544878214597702,0.008059719494458861,2.6412010779604316,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",20,0.9907596381381154,-0.5422546863555908,-0.5164472460746765,0.8210398592054844,0.0607327943926316,0.20842191204428673,0.06716034899754603,0.008997775614261627,0.01751831916461361,1.9519575769081712,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",40,1.9775067050941288,-1.0624051094055176,-1.0102983713150024,0.7194780558347702,0.07275428926920935,0.12411035550758243,0.020499000021128096,0.004693526774644852,0.007195600858653873,2.2115360940806568,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",90,4.434042513137683,-1.3600634336471558,-1.2923141717910767,0.6379435323178768,0.06649943762186665,0.08887315727770329,0.024044681767733545,0.0015543736517429352,0.0030473164703687257,2.635578839108348,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",10,0.48880934133194387,0.28645920753479004,0.2846434712409973,0.9127521067857742,0.056309572749958445,0.4380400376394391,0.16577200819273197,0.0379902683198452,0.06945571021547119,1.5115902530960739,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",60,2.8937653033062816,-1.2356740236282349,-1.1845760345458984,0.7028007730841637,0.07624144298959117,0.12878703698515892,0.07631426934348859,0.0024297982454299927,0.003079135107031809,2.2786553830374032,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",70,3.3665533389430493,-1.302201271057129,-1.250348687171936,0.6583734191954136,0.06930124262804997,0.09795180056244135,0.026150284667012386,0.0013666413724422455,0.003211953907161277,2.3330645258538425,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",10,0.4762559151276946,0.2678060829639435,0.2647700905799866,0.9129333160817623,0.056444988621361936,0.45552707090973854,0.18501619108780215,0.03457101434469223,0.06235781895002697,1.8052906589582562,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",80,3.778681771364063,-1.2957391738891602,-1.2591619491577148,0.6759349890053272,0.07204795245152566,0.10143298143520951,0.03546394958226565,0.00290057435631752,0.008078048683959402,2.3320356409531087,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",120,5.654541203053668,-1.3496723175048828,-1.3191049098968506,0.667296577244997,0.06102533859207442,0.12451009824872017,0.08418269273142982,0.0018051043152809143,0.002384760483742615,2.3525110920891166,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",130,6.121312269940972,-1.2169288396835327,-1.1738767623901367,0.641562856733799,0.059499909890561475,0.14146011462435126,0.08411094553494682,0.007955651730298996,0.0196577277593593,2.470489911036566,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",20,1.3517721861135215,-0.95738685131073,-0.9135687351226807,0.7586062103509903,0.05359918979056795,0.19997819559648633,0.06901459994557926,0.011886104941368103,0.014474648636804565,2.8775183099787682,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",70,4.720852294936776,-1.1988781690597534,-1.1156272888183594,0.6660712473094463,0.07027168665344127,0.1140854936093092,0.03361524657253939,0.011531636118888855,0.017732982874655443,3.757806320907548,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",20,1.3543319499585778,-0.6059620976448059,-0.5953549742698669,0.8027100563049316,0.06571783727727026,0.17527318140491843,0.029708504935615502,0.010197903960943222,0.01317353090037238,3.1663036139216274,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",40,2.697801168076694,-1.100635290145874,-0.9911084771156311,0.6840195469558239,0.060486464763048996,0.13927119271829724,0.021159439211753776,0.01501951739192009,0.017884533660072297,3.6077506749425083,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",0,0.0020980481058359146,2.9621520042419434,2.8653225898742676,0.985351774841547,0.006004974681296067,1.3572011142969131,0.34484139565111155,0.3004272896796465,0.20260291728455745,1.393240809906274,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",50,3.3866087831556797,-1.0894721746444702,-0.9324155449867249,0.7359279803931713,0.060082419387489115,0.10919538559392095,0.046981867166767374,0.006501454859972,0.011917305822263226,4.168476685881615,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",30,2.052955917082727,-0.9205144643783569,-0.9046874642372131,0.7265635319054127,0.03838729938544722,0.1355988159775734,0.02525156205372638,0.01751837134361267,0.03236926429717494,3.3116175229661167,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",40,2.7323181910905987,-1.1685011386871338,-1.100807547569275,0.6893590055406094,0.05046477677422718,0.1424742853268981,0.05130107635067308,0.018553398549556732,0.028511606445481372,3.6809552561026067,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",90,6.156599438050762,-1.2505239248275757,-1.224821925163269,0.67469572275877,0.038967926727516734,0.11526535032317042,0.02866863981484119,0.00820641964673996,0.012335162138048465,4.044173894217238,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",150,10.257646234240383,-1.339490532875061,-1.3563295602798462,0.652554415166378,0.05373584843272267,0.14044713648036122,0.0928598529197162,0.007503323256969452,0.01448357631902781,4.169651726959273,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",30,2.0342956804670393,-0.9719418883323669,-0.9755501747131348,0.7345871105790138,0.06610899074512032,0.1913120816461742,0.11792074045202061,0.011612042784690857,0.01767187665290341,3.35310638602823,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",40,2.7071231831796467,-1.291792869567871,-1.2486439943313599,0.6827063336968422,0.06746874590118916,0.12156684184446931,0.02972290417111378,0.005703967064619064,0.007827140179061852,3.6883867199067026,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",10,0.6795864054001868,0.32224100828170776,0.30641409754753113,0.9107981286942959,0.04547909639786297,0.4702867539599538,0.18461484313520957,0.07530572824180126,0.10080489253363002,2.9289341140538454,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",60,4.068608966190368,-1.2536441087722778,-1.2171974182128906,0.6908110938966274,0.06338741703862286,0.11859154189005494,0.04666416328385473,0.007781703025102615,0.013133657125342337,3.741353678982705,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",110,7.443953653797507,-1.1768159866333008,-1.183288335800171,0.7144748494029045,0.052017516333346996,0.11878595454618335,0.03028286356646534,0.005499955266714096,0.006164043753355097,4.063772855093703,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",0,0.0018613580614328384,2.9210941791534424,2.849660873413086,0.9838560111820698,0.007394744312073832,1.279889740049839,0.2764682504519883,0.2767325695604086,0.18035397853669585,1.4009219210129231,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",60,3.965137240709737,-1.3146979808807373,-1.2572499513626099,0.6802009679377079,0.06354448730517777,0.13820835947990417,0.08469771242920926,0.005525309592485428,0.008024683434142872,3.6038124160841107,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",10,0.6529715738724917,0.31443044543266296,0.3308085799217224,0.9182726293802261,0.04646942195920004,0.47471717931330204,0.19355587442858063,0.07648971676826477,0.107766630236161,2.8549183120485395,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",60,3.948542777216062,-1.1723642349243164,-1.1383213996887207,0.7073369063436985,0.06273920687348965,0.12009957898408175,0.06953944375490291,0.005270533263683319,0.008568327380870967,3.6915907161310315,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",70,4.603380448184907,-1.2286503314971924,-1.1936168670654297,0.7129817642271519,0.0747168921861196,0.1171685354784131,0.056252936404725104,0.006452742964029312,0.011546882704225598,4.087200474925339,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",10,0.6634443767834455,0.3387714624404907,0.31514108180999756,0.9198887087404728,0.04311032358633392,0.460804277099669,0.1724010995867532,0.07156917825341225,0.09980570596418575,2.7336709790397435,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",60,3.9753512856550515,-1.0729979276657104,-1.1135437488555908,0.730590146034956,0.05524920136106077,0.11283030128106475,0.021609292132176057,0.008571397513151169,0.011358900795730254,3.85538076213561,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",110,7.335607598070055,-1.1998047828674316,-1.251206398010254,0.6917014978826046,0.04929515273026889,0.11392170819453895,0.040402860047521735,0.007781568914651871,0.01595990188151064,4.115949971135706,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",120,8.013796475715935,-1.196070909500122,-1.0998737812042236,0.6877105087041855,0.056392244096178566,0.12900797137990594,0.06005284708106751,0.00454011932015419,0.005221601564128821,4.339898922946304,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",0,0.0018707369454205036,2.924889087677002,2.8505842685699463,0.9833973571658134,0.008836800095493202,1.301365103572607,0.28376045902045394,0.28039319440722466,0.18111543940015049,1.3837637030519545,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",50,3.337458040099591,-1.2317492961883545,-1.1922248601913452,0.6836216412484646,0.0542091183955914,0.14849703526124358,0.04690795700507729,0.009898364543914795,0.010739159329440801,3.6371351641137153,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",70,4.697896077530459,-1.278668761253357,-1.233733892440796,0.6490102186799049,0.06662988590978461,0.12501557311043143,0.05225081874055212,0.00859842449426651,0.01619708155303429,3.510975951096043,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",120,8.061222599353641,-1.3943501710891724,-1.351938009262085,0.6301411874592304,0.0682058678475949,0.09135631751269102,0.03150027227048242,0.0025474801659584045,0.004320583967496328,3.9440689589828253,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","fmpe_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Flow matching posterior estimator (FMPE) [1,2].\n\n    The solver trains a regression network to approximate a vector field inducing a\n    time-continuous normalizing flow between the posterior distribution and a standard\n    Gaussian distribution.\n\n    Implemented with the :mod:`lampe` package.\n    ",170,11.409269873285666,-1.3794045448303223,-1.3455452919006348,0.6556116230785847,0.06014963882348391,0.09687103680334985,0.04264713876397009,0.007604159414768219,0.016854093262759662,4.285026384983212,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.008045620052143931,2.915389060974121,2.8478164672851562,0.9852602444589138,0.006914134755102211,1.3660758882761002,0.29335501771585665,0.27676475420594215,0.18473337734992443,0.041684784926474094,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,2.6526343177538365,-0.171259805560112,-0.1811067759990692,0.8793610967695713,0.06362158990358048,0.41742990585044026,0.21544207787900604,0.05889795161783695,0.09033674870746693,0.01722872513346374,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,3.172232821350917,-0.20623765885829926,-0.22371546924114227,0.869502954185009,0.06344470270038076,0.40900878701359034,0.21193386938741418,0.05923135578632355,0.09003870284385818,0.01737258699722588,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",120,6.2839235556311905,-0.3663792014122009,-0.36505362391471863,0.8586690500378609,0.07664917822327294,0.40107712568715215,0.2255850356453803,0.05351921170949936,0.08906319798675928,0.017541635781526566,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",170,8.888552092947066,-0.46315863728523254,-0.4829319417476654,0.8507651053369045,0.08212311619617568,0.4059099839068949,0.24110811445584637,0.05482378043234348,0.0926280080729832,0.017540371045470238,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",220,11.485124809667468,-0.4906211495399475,-0.5370244383811951,0.8454859405755997,0.06911368754980897,0.3965693237259984,0.2253228534517949,0.05408535711467266,0.08875398308048987,0.017218536930158734,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",230,12.003124482929707,-0.4628809094429016,-0.5312219858169556,0.8502796031534672,0.06283944478560916,0.3996349833905697,0.23509128523482636,0.056682391092181206,0.09357746451877848,0.0175073288846761,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",280,14.603270767023787,-0.5083907246589661,-0.5621125102043152,0.83522929251194,0.0837608813862317,0.41086826752871275,0.24728859771872772,0.05584445968270302,0.09211562509432404,0.017224408918991685,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",330,17.19519601133652,-0.5335835814476013,-0.5760234594345093,0.8312379978597164,0.09182551948010036,0.41881169052794576,0.24462723777254383,0.05711905658245087,0.08965380875306575,0.0172282790299505,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,2.607813422102481,-0.16817960143089294,-0.18366661667823792,0.8744816333055496,0.0689845865635369,0.40959391836076975,0.2164519882522095,0.058570291846990585,0.0888166383654228,0.017432810040190816,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,3.1345565444789827,-0.20138250291347504,-0.2034413367509842,0.8800058923661709,0.057775497578136784,0.4089032714255154,0.2242432237250376,0.054168710485100746,0.08840954644757469,0.017302127089351416,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",110,5.727534382138401,-0.3653391897678375,-0.36331698298454285,0.8573605269193649,0.07777303122697471,0.39885196182876825,0.22333691926972588,0.05328051745891571,0.08801666624253207,0.017427912913262844,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",160,8.288437730865553,-0.41002047061920166,-0.4399341940879822,0.8517486155033112,0.06839254218552973,0.39390189247205853,0.22848668266105707,0.05249684490263462,0.08741260049598439,0.017354530980810523,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,1.0311587573960423,0.005071723833680153,-0.011751530691981316,0.8928838111460209,0.05298019552443683,0.42781397281214595,0.21555864461940888,0.0640973374247551,0.09115702653326924,0.017537483014166355,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,1.5439637517556548,-0.07171521335840225,-0.08024628460407257,0.8778677843511105,0.07055281994939024,0.4164243903942406,0.2242533015001767,0.058156002312898636,0.08952916298504718,0.01732227997854352,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",90,4.613872761139646,-0.32940709590911865,-0.345501571893692,0.8590677753090858,0.07339637243650021,0.399955153465271,0.22438739867573837,0.0542774423956871,0.09028634635228809,0.017138965893536806,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",140,7.152564145624638,-0.4134582579135895,-0.44334331154823303,0.8557141162455082,0.06822649626049827,0.3934011575765908,0.2240803965475422,0.05445368401706219,0.0902836482868372,0.016980868065729737,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",190,9.659282105509192,-0.4673747718334198,-0.501733124256134,0.8495750278234482,0.06988016314500296,0.38935868302360177,0.22590992615378705,0.05174613930284977,0.08837536048916037,0.016529754968360066,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",200,10.15869975835085,-0.4527193009853363,-0.5086023807525635,0.8460011072456837,0.06543214414238291,0.3932616114616394,0.22425784693375184,0.05477602034807205,0.08885039878420459,0.016628615790978074,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,1.0062928842380643,0.031221799552440643,0.005500464700162411,0.8885822035372257,0.0541582671543608,0.4224469168111682,0.21195120629176598,0.06330147944390774,0.09171012933828425,0.016646406846120954,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",80,4.006270015612245,-0.28519803285598755,-0.28570830821990967,0.8732905946671963,0.06923014355833543,0.4059284748509526,0.228988651131965,0.05575641058385372,0.08908235033698216,0.01643990003503859,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",130,6.5124727946240455,-0.3355288505554199,-0.37948018312454224,0.8615141734480858,0.06894135067828489,0.3981334432028234,0.22679232214783498,0.05562448315322399,0.08943587959155175,0.016726681031286716,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",140,7.015557728940621,-0.40495020151138306,-0.4280988872051239,0.8501911275088787,0.07539648466083239,0.40082507114857435,0.2252360296334334,0.05409947782754898,0.08420006157978285,0.016658375039696693,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",190,9.518507152795792,-0.42302384972572327,-0.46314042806625366,0.8503674194216728,0.06843767762972991,0.39959151530638337,0.23657577450010434,0.05230256728827953,0.0892542092300492,0.016682639019563794,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",240,12.013082434656098,-0.49851691722869873,-0.5498663187026978,0.8468721993267536,0.06294085908039404,0.40473866974934936,0.23559775111425216,0.05494228936731815,0.09070522971552275,0.016722312895581126,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",290,14.509650589898229,-0.4934196472167969,-0.5572527050971985,0.8407787382602692,0.08771322276370945,0.41980926133692265,0.23830679918541275,0.060503436252474785,0.09324391146073628,0.016677378909662366,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.5324470470659435,0.14468932151794434,0.13008294999599457,0.9089964404702187,0.04223926350560919,0.4623488234356046,0.20699683367255844,0.07349282875657082,0.09518499357409213,0.01704676216468215,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,3.0308649393264204,-0.17388710379600525,-0.20550447702407837,0.8704852648079395,0.06830147387760992,0.4107542810961604,0.21197700951920087,0.06114913709461689,0.09219865465162937,0.016644496005028486,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",110,5.532299783080816,-0.40409380197525024,-0.40637972950935364,0.8585463464260101,0.06514240934384935,0.3995361039415002,0.22613517448280976,0.05607650987803936,0.09386951496158169,0.016539181815460324,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",160,8.008642078144476,-0.4705144762992859,-0.4980219006538391,0.8464921116828918,0.0759150364778035,0.3898913045413792,0.2197109588793339,0.05144442804157734,0.08798619255540417,0.016348620876669884,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",170,8.499912473373115,-0.47600048780441284,-0.5173097848892212,0.8380406573414803,0.08964740484424796,0.38760965038090944,0.22376979155713922,0.05038318410515785,0.08722971518373866,0.01638285513035953,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",220,10.960131976986304,-0.4939117133617401,-0.5478213429450989,0.8321139439940453,0.09174792316085853,0.38801622530445457,0.22200278461093823,0.051693933084607124,0.0891446861096911,0.01633672695606947,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",280,13.872895623091608,-0.5406969785690308,-0.5801389217376709,0.8388990759849548,0.08309272636193622,0.3989657899364829,0.23558992673380366,0.05224468372762203,0.08905875934063019,0.016094388905912638,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",330,16.288000844884664,-0.49653881788253784,-0.5453624725341797,0.8419787250459194,0.07648839367782918,0.3908603470772505,0.23966680355433115,0.05122731439769268,0.08908875660178076,0.016111622098833323,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",340,16.766502169892192,-0.5364232063293457,-0.589911937713623,0.8323354125022888,0.09384499304842996,0.39239901350811124,0.2277897748609815,0.05276966840028763,0.08895809853239803,0.016005695099011064,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,1.9208021755330265,-0.12039107084274292,-0.14754077792167664,0.8699958920478821,0.06974580055284636,0.42662502685561776,0.21742360313043543,0.06135778687894344,0.08897056659566809,0.01570080895908177,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",90,4.291893315734342,-0.308695524930954,-0.3108178377151489,0.8558617159724236,0.07885431992765576,0.4031816441565752,0.2176602706006572,0.05682359263300896,0.08755255035996268,0.015207964926958084,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",140,6.651709159603342,-0.3574131727218628,-0.3839934766292572,0.8581210784614086,0.08094961523032249,0.40865987865254283,0.23035539967521101,0.0587142389267683,0.09303078629841122,0.015090652974322438,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",150,7.122164063155651,-0.3740842938423157,-0.41872844099998474,0.8495421521365643,0.0785527982591226,0.4163398640230298,0.22825281061357852,0.061095450073480606,0.09232496142908378,0.014975887024775147,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",210,9.945211592363194,-0.48249396681785583,-0.5068702101707458,0.8496664986014366,0.06955696381408545,0.4096011146903038,0.24298713036782263,0.057055575773119926,0.09340914538288143,0.015040143858641386,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",260,12.296839772723615,-0.49073103070259094,-0.5333208441734314,0.8485132530331612,0.06108225731104023,0.40542241325601935,0.2384568830646838,0.05420194938778877,0.09005321841590805,0.01488949591293931,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",310,14.65084538096562,-0.48212042450904846,-0.5179898738861084,0.8475929535925388,0.0741287872085841,0.4009576253592968,0.2433424181479598,0.05535949766635895,0.09322255646007878,0.015004681888967752,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.0023004862014204264,2.927931785583496,2.8388142585754395,0.984892450273037,0.005171265365306434,1.334476001560688,0.2884161992553632,0.27352251298725605,0.18774438421931874,0.01526201399974525,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,2.3469399826135486,-0.1389351487159729,-0.16390946507453918,0.8854385353624821,0.04757371233570826,0.42352462466806173,0.22901535774620832,0.05905824899673462,0.09026276838859314,0.015048572095111012,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",110,5.171118718804792,-0.307140588760376,-0.3173059821128845,0.8631573058664799,0.07457453492833718,0.40041354577988386,0.2199388766800633,0.056273264810442924,0.08894507220265559,0.014984212117269635,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",160,7.521417133510113,-0.3852047324180603,-0.40511399507522583,0.8657837361097336,0.05632217123344873,0.40442514466121793,0.23300869343066863,0.05420536734163761,0.08748315539834162,0.015198611887171865,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",170,7.99164271960035,-0.39884841442108154,-0.43223509192466736,0.8583368882536888,0.06435252571995263,0.4005980002693832,0.2385852082459228,0.05360423028469086,0.08937417258659569,0.015002646017819643,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",220,10.340674124425277,-0.4197816848754883,-0.4616287350654602,0.8606592491269112,0.059678972987113874,0.4080319809727371,0.25048424476737663,0.05198527313768864,0.088482604583549,0.015049994923174381,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",270,12.684217449044809,-0.4915759861469269,-0.5281761884689331,0.8540313877165318,0.07417037152712345,0.41190766263753176,0.24828501803541225,0.05601796880364418,0.0912641481336487,0.015082164900377393,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",330,15.500822585308924,-0.48658865690231323,-0.5391140580177307,0.8398116230964661,0.07636511956171485,0.4098453694023192,0.24858338663410615,0.05294471234083176,0.08764601294197233,0.015001269988715649,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",340,15.973510790150613,-0.525587797164917,-0.5694595575332642,0.8370961286127567,0.08367601777129156,0.42055833898484707,0.25833920259459375,0.05702726170420647,0.09287122556045656,0.015120478812605143,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.47293890709988773,0.16599377989768982,0.16760799288749695,0.9074688367545605,0.05007853250937858,0.4709100406616926,0.21904628332382434,0.07370612770318985,0.09881295431349196,0.015030191978439689,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,2.831311499932781,-0.20530013740062714,-0.218891441822052,0.8673377931118011,0.08242280209697839,0.41875444911420345,0.22105842069392656,0.061311909928917885,0.09326284761307482,0.014889311045408249,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",110,5.173361136578023,-0.35113033652305603,-0.34986868500709534,0.8631295822560787,0.06719374906627884,0.4101453786715865,0.22689525307679503,0.05567715875804424,0.09094255493011119,0.014879046939313412,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",120,5.641714238096029,-0.3484877347946167,-0.36316636204719543,0.8621801435947418,0.06799437415839937,0.40978353656828403,0.23126223044939345,0.057856252416968346,0.09330709856425165,0.014973063953220844,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",180,8.463262075325474,-0.44745805859565735,-0.43669191002845764,0.8434143327176571,0.10055602509908486,0.3967380356043577,0.22953904581042825,0.05168335512280464,0.08669908321720038,0.014951301971450448,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",230,10.819646953139454,-0.43906038999557495,-0.47508862614631653,0.8477826565504074,0.07853885290356792,0.407786020077765,0.24228438330361216,0.05705820769071579,0.09230720168990572,0.014942103996872902,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,1.4105559727177024,-0.11552605777978897,-0.126582533121109,0.8837584182620049,0.058663842588951846,0.4166770363226533,0.21267129909665938,0.05889185331761837,0.0876370748813767,0.014729722868651152,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,1.878507356159389,-0.1468353420495987,-0.16145364940166473,0.8845821879804134,0.053675661572397046,0.40510362992063165,0.2105120353619476,0.05608023516833782,0.08388560223639019,0.01480243680998683,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",90,4.233572184341028,-0.2469576746225357,-0.2903575599193573,0.856659535318613,0.07592642009548276,0.4000006811693311,0.2074859141091192,0.05596556141972542,0.08433448700417635,0.015102776931598783,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",140,6.590076972497627,-0.399035781621933,-0.3872205317020416,0.8630972392857075,0.060435241887814614,0.39492114540189505,0.22391785848315485,0.050883812829852104,0.08630899998409483,0.014958404004573822,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",200,9.432858408195898,-0.4482923150062561,-0.4875398576259613,0.842407114803791,0.07081654324736916,0.3964763875119388,0.2314619375776128,0.052354903891682625,0.08425016462604598,0.014968862989917397,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",210,9.90252226521261,-0.4640200436115265,-0.504214346408844,0.8488123938441277,0.06100453944096993,0.38981730258092284,0.2329288997894992,0.052160533145070076,0.08834785260531483,0.014984461013227701,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",260,12.259952252265066,-0.5067979693412781,-0.5415874719619751,0.8433502987027168,0.0724198659506365,0.406504413112998,0.2509499095906603,0.05358528532087803,0.08784586328407537,0.014803668018430471,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,0.94908188120462,0.01811501383781433,-0.0011040461249649525,0.8867486119270325,0.06034782062997258,0.4453270020894706,0.22321261744652682,0.0677433218806982,0.09362174761368248,0.014915297040715814,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",70,3.3164567302446812,-0.263472318649292,-0.29638129472732544,0.8626994006335735,0.06936249142174494,0.41872511245310307,0.23543680206277917,0.0621772687882185,0.09564905305939754,0.014815909089520574,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",80,3.787005537888035,-0.29362159967422485,-0.32599443197250366,0.8480152301490307,0.08715243224091698,0.40642600134015083,0.228654050347386,0.05927112326025963,0.090526918913088,0.015645195031538606,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",130,6.1469007367268205,-0.3172529339790344,-0.38903021812438965,0.8447477482259274,0.08687689182849828,0.40162352146580815,0.23487212901671017,0.054986342787742615,0.08674713213950108,0.015501381829380989,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",190,8.996101291151717,-0.4252604842185974,-0.47183412313461304,0.8293809294700623,0.11286112080504564,0.4152139755897224,0.2571465058907858,0.05607203394174576,0.08988304011951728,0.014995785895735025,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,3.7963531350251287,-1.5851026773452759,-1.517311692237854,0.6644271425902843,0.07015928434450028,0.11904880637302995,0.03313649947135814,0.006172992289066315,0.007401893057055696,0.04807128198444843,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,5.032273224787787,-1.461564064025879,-1.5288605690002441,0.7005385607481003,0.0416828530921212,0.10774673521518707,0.030742022990405526,0.004649769514799118,0.006108493539302825,0.0479840689804405,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",90,11.207708476809785,-1.8290696144104004,-1.8512165546417236,0.6032103784382343,0.03743364417512208,0.09419327089563012,0.02850119907443665,0.004515208303928375,0.0051451840864793,0.04829780198633671,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",140,17.407806131755933,-1.9310277700424194,-2.0751805305480957,0.571712389588356,0.03820530739168045,0.08701251307502389,0.047213717482095824,0.003095850348472595,0.004000557738692073,0.04735615313984454,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",200,24.81356274569407,-1.9683082103729248,-2.156609058380127,0.5530093517154455,0.0490299641192151,0.08851559017784894,0.06600861732183952,0.003154441714286804,0.005813641203406175,0.04793544998392463,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",210,26.046831344021484,-1.9612765312194824,-2.1533966064453125,0.5626158304512501,0.03496758155833685,0.09877785528078675,0.06611048529728314,0.004397943615913391,0.004943030163590552,0.04780033603310585,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,4.945838717278093,-1.563509464263916,-1.5651668310165405,0.6575904935598373,0.0569507665418918,0.12191528314724565,0.04736260833914582,0.005989212542772293,0.009096813713361806,0.04845377220772207,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",90,11.119078575400636,-1.8381295204162598,-1.8804616928100586,0.6091622114181519,0.04571029248290109,0.10239938413724303,0.03660615749458995,0.005510289222002029,0.007242821725166645,0.047662327997386456,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,1.2378708701580763,-0.7232888340950012,-0.7561901807785034,0.7833280153572559,0.04968076533088623,0.2324265972711146,0.10895182723822022,0.03430069983005524,0.03801664985770366,0.04709192807786167,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,3.7029294935055077,-1.4371628761291504,-1.4614566564559937,0.6882315576076508,0.05115845215984586,0.13975707115605474,0.046740422487581305,0.009302739053964615,0.01181785636764378,0.047762606060132384,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",80,9.88495318335481,-1.693215250968933,-1.6075599193572998,0.6190375089645386,0.04855467244896702,0.12468873849138618,0.05699390305186649,0.007074914872646332,0.008936645635963004,0.04754117107950151,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",130,16.077207691268995,-1.8815611600875854,-1.8916847705841064,0.6033321917057037,0.04446784336141228,0.11084534646943212,0.04338747359417759,0.007139425724744797,0.007315231805348188,0.04793676105327904,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.004687847103923559,2.873267889022827,2.7917673587799072,0.9846794866025448,0.006471093858300692,1.3373578265309334,0.2906906127209144,0.3381612319499254,0.17528114749963522,0.04782850295305252,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,1.2397457417100668,-0.9508202075958252,-0.9202067852020264,0.7795377671718597,0.04294551669406313,0.1821505236439407,0.06174974547894433,0.016466520726680756,0.01648565163091068,0.048294798005372286,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,7.386481606634334,-1.6715205907821655,-1.7225604057312012,0.625603936612606,0.04604639243040756,0.09760313294827938,0.026364688921868492,0.00410909578204155,0.005870192669823196,0.04691616795025766,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,1.2319579261820763,-0.8287929892539978,-0.830047607421875,0.7900707311928272,0.036808373144785786,0.2130212946794927,0.08596271567382842,0.026850692927837372,0.02727670765639121,0.04740282287821174,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,7.340316980844364,-1.6458611488342285,-1.7080309391021729,0.645835991948843,0.06272102623219465,0.11555530270561576,0.04333632369836595,0.007085464894771576,0.009804250101641692,0.046700494131073356,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",70,8.555223733652383,-1.6519032716751099,-1.7535629272460938,0.6344006136059761,0.0454307383745749,0.10995744727551937,0.035019547668192416,0.0055316053330898285,0.006625605088456777,0.04654343193396926,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",120,14.596840520389378,-1.5383572578430176,-1.6536729335784912,0.6603408791124821,0.0625175105317481,0.10529342596419156,0.05106187175329811,0.006401225924491882,0.01003051758720678,0.0465313489548862,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",170,20.58931220183149,-1.9146677255630493,-2.031982421875,0.5876721851527691,0.03366241357689419,0.08695785515010357,0.02666888647017838,0.005202744156122208,0.007275913691632259,0.04587681102566421,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,2.397810189286247,-1.2915667295455933,-1.2868772745132446,0.7036138065159321,0.061706152682256994,0.15340701257809997,0.06301463194492882,0.011064797639846802,0.015159719051943639,0.04597317706793547,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,3.598063886165619,-1.4800751209259033,-1.4853036403656006,0.6756332367658615,0.05541326095591395,0.1336501780897379,0.05457198938802769,0.008387863636016846,0.011682739345146707,0.04596917307935655,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",90,10.775214205728844,-1.7527461051940918,-1.8244578838348389,0.5969818569719791,0.04818964328293573,0.11995490035042167,0.05776199383225525,0.007253378629684448,0.008077875567878302,0.04612823505885899,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",140,16.768631417537108,-1.9657797813415527,-2.1169965267181396,0.5694922842085361,0.04133638495714823,0.11184623185545206,0.0554951377435425,0.007844973355531693,0.008735607990734456,0.04567338200286031,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",190,22.755984515184537,-2.0001485347747803,-2.180342674255371,0.5604896582663059,0.043454440782260934,0.13289598328992724,0.07610727912280402,0.009033136069774628,0.010158669024759103,0.04561700904741883,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",200,23.956220787251368,-1.7694324254989624,-1.9953902959823608,0.5844714939594269,0.0518801210849871,0.1263160069938749,0.0694934958733845,0.00837136059999466,0.010793496604619453,0.0459791598841548,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",250,29.942852137377486,-1.953331470489502,-2.221250295639038,0.5567331872880459,0.042018320708282694,0.12582698720507324,0.09018010367679626,0.008339673280715942,0.009755234551755768,0.04634333308786154,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,4.802850033855066,-1.5521352291107178,-1.5723096132278442,0.6730887368321419,0.0511985449968494,0.12461932701990008,0.048433872767166236,0.007692914456129074,0.011590772118493707,0.046244712080806494,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",100,12.009982492541894,-1.8431485891342163,-1.9472053050994873,0.5943528972566128,0.052545854188544974,0.10347917024046183,0.05712324975638971,0.006186887621879578,0.01003618295597509,0.04630134114995599,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",110,13.208442088682204,-1.84060537815094,-1.9703168869018555,0.5814499966800213,0.039900578176812564,0.10210844967514277,0.05544478730592286,0.005257703363895416,0.007444886888692465,0.046395041048526764,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,2.4164257992524654,-1.3337297439575195,-1.300676703453064,0.7139883078634739,0.06572356168747863,0.15401855343952775,0.055006642399123874,0.010557044297456741,0.014834025245802324,0.04612031485885382,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",70,8.420752440812066,-1.7287712097167969,-1.7736104726791382,0.629395604133606,0.04096265690501557,0.0978225264698267,0.04982257664986748,0.005130480974912643,0.00792761549667783,0.046368880895897746,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",120,14.425462454557419,-1.851881980895996,-1.9092087745666504,0.6071437671780586,0.049963169484935316,0.10557963559404016,0.0584134795820299,0.0047941505908966064,0.00626728441324666,0.04618984111584723,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",130,15.626730476506054,-1.9875508546829224,-2.082688093185425,0.5747373923659325,0.034371194476406734,0.09850403387099504,0.059051093984310715,0.004238717257976532,0.00535644358914299,0.045353875029832125,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",190,22.780182225862518,-1.9855239391326904,-2.1407456398010254,0.5594085864722729,0.031248335399746596,0.09203556552529335,0.06986195788395655,0.0039052814245224,0.00527326017733449,0.045517709106206894,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,3.5296008645091206,-1.3412656784057617,-1.4487597942352295,0.6662549152970314,0.04730970411871695,0.2037875447422266,0.13585180946335176,0.013368580490350723,0.0151664320117711,0.04543427680619061,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",80,9.398381234612316,-1.8122838735580444,-2.0001559257507324,0.578739870339632,0.037882078733682226,0.10379785671830177,0.04937866218427967,0.0049410127103328705,0.0075228107422509885,0.04480469902046025,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",90,10.561928912065923,-1.9483810663223267,-2.0339553356170654,0.577231865376234,0.03505421390138334,0.10431107971817255,0.05758957972945535,0.00475383922457695,0.006828443460359081,0.04453559499233961,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",150,17.535885748220608,-2.115110397338867,-2.1520445346832275,0.5664650499820709,0.03690408785958479,0.08342574909329414,0.04467915418385378,0.003048475831747055,0.004962467971152875,0.044896849896758795,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,1.1565458783879876,-0.5457247495651245,-0.5527515411376953,0.8288888782262802,0.06785735051886141,0.27445232309401035,0.12704004519233428,0.04835117980837822,0.05576582133067689,0.04407935403287411,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,6.8991116899997,-1.726323127746582,-1.712239146232605,0.6389195062220097,0.05295300172857526,0.11394581710919738,0.04453972876464541,0.006279557943344116,0.00843175551885505,0.04385548993013799,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",70,8.038276170613244,-1.4621326923370361,-1.5458112955093384,0.6600586660206318,0.058922488358924,0.1160592706874013,0.04624600996306714,0.008097227662801743,0.012730653735370159,0.04340374190360308,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.006767782848328352,3.0899062156677246,2.9715375900268555,0.9859626479446888,0.007009270227536527,1.235008716583252,0.368044595532757,0.29939186945557594,0.20375642019339346,0.0833295090124011,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,10.184253439307213,-1.4388312101364136,-1.5302430391311646,0.6343958303332329,0.032248401887541765,0.12386563536711037,0.06774097820727627,0.008890360593795776,0.01285024865172669,0.08499688189476728,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",110,22.22199784219265,-2.0719923973083496,-2.109184980392456,0.5641706436872482,0.036202814664353586,0.0800858810544014,0.03451262443580898,0.0036422759294509888,0.004170312255936515,0.08503353293053806,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",120,24.22329579363577,-2.028111457824707,-2.0786142349243164,0.5801651142537594,0.04449413358307178,0.0931945521151647,0.05275889491946142,0.004250761121511459,0.005512835291270265,0.0847034640610218,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",170,34.256841104477644,-2.1662256717681885,-2.2427978515625,0.5451375339180231,0.027665306557624607,0.07814447837881744,0.04975205952821703,0.0030781738460063934,0.003984772560655985,0.08396067703142762,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,2.011900682002306,-1.0118169784545898,-0.9699558019638062,0.7745956070721149,0.060068474450913054,0.18011661246418953,0.04838866618150884,0.014969300478696823,0.013970681882031466,0.08616648497991264,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,12.032404170371592,-1.6968388557434082,-1.795466661453247,0.6169693283736706,0.06275545249933652,0.10374997998587787,0.042882961399042296,0.005269918590784073,0.005233033824495935,0.0885404720902443,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",70,14.037917788838968,-1.9232178926467896,-1.9533318281173706,0.5796834193170071,0.05998859307792338,0.10781242488883436,0.0549762155797288,0.004626542329788208,0.004268985018005118,0.08615633496083319,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",120,24.112452754750848,-2.0072507858276367,-2.1217172145843506,0.5695853903889656,0.03632310641864693,0.14865098404698074,0.0960283948976625,0.009171120822429657,0.012749152851150239,0.08589756092987955,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,6.020096441498026,-1.6335678100585938,-1.655180811882019,0.6340936347842216,0.03180618144571116,0.1441902406513691,0.08502443815639456,0.007727384567260742,0.00590568662208304,0.08454691781662405,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",80,16.047997418325394,-1.970832109451294,-2.1537060737609863,0.5802232511341572,0.037576819753742614,0.08117284439504147,0.028290709313948406,0.005024489015340805,0.007170860189419781,0.08568888995796442,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",90,18.04729683045298,-1.9793124198913574,-2.1443357467651367,0.566955616697669,0.0463508571231956,0.09428568347357213,0.0496180797703493,0.0036790892481803894,0.0030660411260278143,0.08444619085639715,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,5.993977879639715,-1.4192067384719849,-1.4011409282684326,0.6735856235027313,0.05599567268661804,0.15038348827511072,0.057996837019202815,0.010019052773714066,0.009723814032956635,0.0853659170679748,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",80,16.03626802517101,-1.9468231201171875,-1.9658859968185425,0.5805084593594074,0.04914590442069212,0.08192117209546268,0.03133089441643612,0.0028203465044498444,0.002804312628190102,0.08346337010152638,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",140,28.047455980209634,-2.1054835319519043,-2.1859891414642334,0.545682430267334,0.045277411132529435,0.09234813577495515,0.06462506270824483,0.0020625442266464233,0.0022020633525592476,0.08495449181646109,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",150,30.063082876848057,-2.055567979812622,-2.1373801231384277,0.5626499876379967,0.048347540236446364,0.07215192634612322,0.03567858293982535,0.001987636089324951,0.002859567135226923,0.08473319397307932,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,3.9802915351465344,-1.2170546054840088,-1.23049795627594,0.6999506503343582,0.0434250559604196,0.1620249506086111,0.06878569034454382,0.010324250906705856,0.008708281888359409,0.08644446521066129,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",70,13.964827538700774,-1.9017692804336548,-1.8612115383148193,0.5777857135981321,0.04574582957168667,0.11267627449706197,0.05489109458709635,0.005249205976724625,0.006006513023070726,0.08391914702951908,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",120,23.92320804623887,-2.052825450897217,-2.159395217895508,0.546534825116396,0.041695867771685595,0.08927240874618292,0.05683643548534379,0.0021926984190940857,0.0032969859331011537,0.08464916585944593,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",130,25.917387473396957,-2.043689250946045,-2.137331962585449,0.5572528205811977,0.03572294659270756,0.07736774859949946,0.03973624741688293,0.002760995179414749,0.004104411519808094,0.08362525305710733,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",180,35.87163864937611,-1.7997312545776367,-2.064188003540039,0.5809308849275112,0.05194226960718794,0.09458084753714502,0.04448569541470542,0.004533883184194565,0.004223278393217905,0.08541012415662408,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,9.910847740015015,-1.8128273487091064,-1.8194046020507812,0.5836167223751545,0.05131139006314836,0.12503521284088492,0.05364145362569621,0.006699591875076294,0.007075256334887076,0.08397118304856122,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",100,19.687761442968622,-2.022512674331665,-2.042013645172119,0.5737618543207645,0.0406610036853718,0.10722608724609017,0.04252947383458312,0.008324749767780304,0.00991078347912862,0.08429588912986219,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",110,21.64907819358632,-2.078146457672119,-2.0846285820007324,0.5807539224624634,0.04112055274608998,0.10015137493610382,0.04079235004732094,0.006596721708774567,0.008710231297109503,0.08342303102836013,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,1.9568169249687344,-0.7374193072319031,-0.7697474956512451,0.7886283434927464,0.04522627902817456,0.20222780015319586,0.05242701367470213,0.0232066847383976,0.025241302106435645,0.08219037507660687,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",70,13.666084523079917,-1.9600884914398193,-2.0636298656463623,0.5862686447799206,0.04409371900742867,0.09588705003261566,0.02902891640123953,0.0053055621683597565,0.00541707808584612,0.0852739040274173,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",120,23.401785113383085,-1.9890636205673218,-2.1182196140289307,0.5588038992136717,0.04688073372905948,0.08743587112985551,0.03463393160203494,0.004540573805570602,0.005978951361742193,0.08434778801165521,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.007823962019756436,3.201521396636963,3.0423738956451416,0.9838252179324627,0.008227476877923483,1.3532112054526806,0.42855775035473,0.30532187782227993,0.19529909583397445,0.08361194212920964,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,9.759484198875725,-1.6582057476043701,-1.7668437957763672,0.6211162693798542,0.09365186061029843,0.14422077336348593,0.10644864122812371,0.005415387451648712,0.005453060108799865,0.08251115307211876,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",100,19.563666096655652,-1.743377447128296,-1.9285587072372437,0.5780907459557056,0.04202770368747856,0.11705976352095604,0.07074114436805753,0.007948417216539383,0.010001561077037514,0.08098821411840618,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.006156455026939511,3.019274950027466,2.9708847999572754,0.9863590709865093,0.0053694492986595165,1.473621904850006,0.36082778511551566,0.3042081203311682,0.18777949661835308,0.083207992836833,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,1.9628402008675039,-0.8636343479156494,-0.8819154500961304,0.7864658758044243,0.057067304474679045,0.17607384826987982,0.04867479098958499,0.014694351702928543,0.013377560273391155,0.08407691400498152,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",70,13.661369509296492,-1.7950503826141357,-1.9893434047698975,0.5860558226704597,0.04397122244536028,0.09293962107039988,0.0345645967419982,0.003764234483242035,0.0027070797420367756,0.08205715497024357,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",120,23.39218157529831,-2.0048279762268066,-2.173405170440674,0.5673804320394993,0.041721141357423844,0.1155115372966975,0.07314166651021169,0.005181923508644104,0.005630823131592607,0.08182301791384816,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",170,33.12640711152926,-2.0400657653808594,-2.2485389709472656,0.5365872327238321,0.03655800369512904,0.08895001746714115,0.04576588076068942,0.0031552650034427643,0.0036599943892298486,0.08176715299487114,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",180,35.10385912936181,-1.9560177326202393,-2.1953556537628174,0.5480351448059082,0.039606332251429184,0.0877378094010055,0.045277768064648616,0.0028857290744781494,0.0032059420044321563,0.08235955494455993,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,5.858125761849806,-1.3935432434082031,-1.4335724115371704,0.6665645688772202,0.047179333621052935,0.1446130876429379,0.06937518876227168,0.009534642100334167,0.008937624725152383,0.08248383412137628,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",80,15.552520806668326,-1.8396406173706055,-1.9051408767700195,0.6014681123197079,0.058809785118533624,0.10732043907046318,0.06488907336808601,0.004080582410097122,0.005160452658047505,0.08363531203940511,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",140,27.039211799623445,-1.9057903289794922,-2.0695502758026123,0.5972008593380451,0.06615793445676034,0.08610656973905861,0.0389175066304538,0.0033067427575588226,0.003823908585579076,0.08139657392166555,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",150,28.95114496606402,-1.9987514019012451,-2.1792755126953125,0.5534979775547981,0.038461610843568986,0.08875621343031526,0.045622205752430325,0.003410976380109787,0.004230565469363184,0.08211883809417486,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",200,38.548975312616676,-2.0969159603118896,-2.2208149433135986,0.5533432215452194,0.04357991450931186,0.08900133788120002,0.054571107299868304,0.0033786892890930176,0.004458442264744723,0.08094285195693374,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,3.2791316295042634,-1.3797259330749512,-1.3901073932647705,0.6808447130024433,0.041253293170055653,0.1167732859030366,0.04023936759969673,0.003968417644500732,0.005917477474461508,0.05143039603717625,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",80,8.554823616752401,-1.5388150215148926,-1.5191704034805298,0.6466720551252365,0.04619868467043919,0.09217133233323693,0.018356097610636,0.004407089203596115,0.006560485217230861,0.05093624512664974,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",90,9.634315214585513,-1.675254464149475,-1.6923235654830933,0.6316918917000294,0.031724157976506365,0.08103868598118424,0.0169507059419936,0.002746894955635071,0.005294176365550073,0.050941915018483996,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",150,15.952046072576195,-1.7855720520019531,-1.7941004037857056,0.5947911962866783,0.039689963907641616,0.10101823089644313,0.029629852609322476,0.007730033248662949,0.014274441031534923,0.050849919905886054,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",200,21.2340835807845,-1.8997334241867065,-1.9165879487991333,0.5958228670060635,0.03631183373660794,0.08334640343673527,0.024067617939043086,0.0037575289607048035,0.00854072367427768,0.0509112598374486,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,3.169222122989595,-1.4570988416671753,-1.4549793004989624,0.6246911771595478,0.049615886073910556,0.1374895041808486,0.05315707723929933,0.004881095141172409,0.004777212441003524,0.051660713041201234,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,4.2212322449777275,-1.5433106422424316,-1.534460186958313,0.6281135492026806,0.04283606782368819,0.10348881944082677,0.038771781932652294,0.003163635730743408,0.004143062306372595,0.051109808031469584,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",90,9.51212051231414,-1.6811896562576294,-1.6516069173812866,0.6171218529343605,0.04700994562558706,0.09876508079469204,0.02586820505698204,0.0033858902752399445,0.005551641818864914,0.052845507161691785,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",140,14.783485133899376,-1.8071234226226807,-1.7788670063018799,0.5931899659335613,0.057025161744353806,0.08214720175601542,0.016342420385042765,0.0021454840898513794,0.003509049980676542,0.05185728217475116,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.0030358468648046255,3.017352342605591,2.923957109451294,0.9859919510781765,0.005335225736732483,1.3391115963459015,0.3159739095749395,0.26693956181406975,0.19589661169587522,0.05080769001506269,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,1.0576637426856905,-0.7673092484474182,-0.733041524887085,0.790307492017746,0.05197832495091998,0.19962755870074034,0.039787267321710264,0.02108863741159439,0.028516434690482113,0.05126247997395694,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,6.334829840576276,-1.7272734642028809,-1.7445441484451294,0.6273180358111858,0.03891459925654689,0.09375738236121833,0.024154526670630123,0.004292301833629608,0.007324477541929343,0.05113310902379453,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",110,11.569721133215353,-1.9304943084716797,-1.9432611465454102,0.5946700051426888,0.0453132682796531,0.08263446064665914,0.02271421150340349,0.002438269555568695,0.003576698224093251,0.05212427885271609,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",170,17.83223775285296,-1.9580070972442627,-2.0342700481414795,0.5866722092032433,0.03602886446724821,0.0910351115744561,0.034187339571245035,0.002450335770845413,0.0031977878372921563,0.05277749104425311,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",180,18.881169906351715,-2.0691111087799072,-2.1043527126312256,0.5637176502496004,0.04410159157694563,0.0874210842885077,0.040361628550963695,0.0020087771117687225,0.0028954050206217823,0.05216951994225383,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,1.0467667896300554,-0.9408459067344666,-0.9496963024139404,0.7452749125659466,0.046120983854325746,0.1754891239106655,0.0496047350852089,0.01132950559258461,0.01499863718385255,0.051151599967852235,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,6.257037072209641,-1.6209989786148071,-1.619950532913208,0.6321366839110851,0.05631071075527312,0.09549041418358684,0.03040909043001956,0.002855893224477768,0.004933884955313985,0.05109214596450329,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,3.10315899271518,-1.522537112236023,-1.4996953010559082,0.644191961735487,0.05616638167908,0.13526744255796075,0.07370214087769907,0.0032370612025260925,0.00432230719010465,0.050751522881910205,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,4.126360311172903,-1.6089868545532227,-1.6049286127090454,0.6260282397270203,0.04866918910130073,0.10102254361845553,0.0406047016556339,0.0021284520626068115,0.003012603931156885,0.04996920400299132,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.003172936150804162,2.9039862155914307,2.8292579650878906,0.9864799790084362,0.003452201413068836,1.3818913511931896,0.34121973765903413,0.26778364926576614,0.1936389147236473,0.05183377908542752,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,6.155265892390162,-1.4330296516418457,-1.4789921045303345,0.6609321534633636,0.04209368803836566,0.13395501673221588,0.06845113758437084,0.0070967525243759155,0.010033887416338094,0.05048018600791693,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",110,11.268668591044843,-1.6957118511199951,-1.7143776416778564,0.6300918459892273,0.044976734815911676,0.12019670894369483,0.06363503023402815,0.004395097494125366,0.005489418947784502,0.050352185033261776,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",120,12.301674864953384,-1.6947815418243408,-1.7356007099151611,0.6177611276507378,0.04101239609409875,0.11763072479516268,0.059383634734412834,0.007350381463766098,0.011251308822611949,0.04853190016001463,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,2.051519075874239,-1.261100172996521,-1.2896519899368286,0.6952187791466713,0.05783598988192165,0.12979572359472513,0.040201042684741055,0.009080298244953156,0.012871201603164916,0.049104245845228434,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",70,7.163334833690897,-1.6310625076293945,-1.6658142805099487,0.6205987967550755,0.03734223493340721,0.09501766273751855,0.02630825646639632,0.0029522590339183807,0.004330150041037956,0.04817524319514632,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,3.091603821143508,-1.4902100563049316,-1.4468421936035156,0.6337556689977646,0.05334089984562162,0.10228068521246314,0.030501543153390424,0.004133947193622589,0.006369892539088964,0.050517236115410924,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,5.133242892101407,-1.6174274682998657,-1.5935472249984741,0.6195570640265942,0.05225011850612788,0.08846983104012907,0.027097814597087152,0.0023742318153381348,0.004136848570350854,0.05097495508380234,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",100,10.262246664380655,-1.7626522779464722,-1.786029577255249,0.5979642495512962,0.06174469441372182,0.09396079275757074,0.03927116169017092,0.0032425634562969208,0.005124567302016029,0.050788554828613997,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",150,15.35441370261833,-1.912414312362671,-1.9085532426834106,0.5932566709816456,0.041555290464872205,0.08566670352593064,0.02825999670604439,0.0026214681565761566,0.004500257974011726,0.04922268493101001,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",200,20.408853913890198,-1.951829433441162,-1.9668842554092407,0.5812667794525623,0.0485742429137136,0.08572349511086941,0.03365082105478521,0.0020212307572364807,0.002676269428874494,0.0494313258677721,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",210,21.423533354653046,-1.8503906726837158,-1.8875209093093872,0.5818475354462862,0.04239304819649311,0.09081047982908785,0.03962173184724403,0.0018393956124782562,0.002584200113191309,0.051024166867136955,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,4.011669467901811,-1.456353783607483,-1.4807878732681274,0.6328335031867027,0.038909977019439136,0.11209763819351792,0.04786421697867283,0.0063385628163814545,0.012234179437151672,0.04653134010732174,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",100,9.967496041208506,-1.7719146013259888,-1.7842210531234741,0.572849752381444,0.05280402178776803,0.08482978353276849,0.03617149765665833,0.0016831308603286743,0.002807131541641133,0.047294692136347294,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,2.948921052739024,-1.3475834131240845,-1.4132707118988037,0.6464173421263695,0.06230762580050086,0.1402365688700229,0.06712108674477361,0.005590509623289108,0.0085760685169915,0.04791714600287378,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,3.9247772737871855,-1.479453444480896,-1.5476478338241577,0.6161162778735161,0.05426263684599542,0.13386188191361725,0.08908287065182614,0.0039096400141716,0.003808716460009748,0.04766221693716943,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",90,8.81633378053084,-1.6786115169525146,-1.734666109085083,0.5931398645043373,0.05815088818336995,0.11310146516188979,0.05794838039871933,0.004000455141067505,0.0058937446138102945,0.04633817495778203,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",140,13.665752541972324,-1.738016128540039,-1.7699655294418335,0.5955791212618351,0.04763111691365165,0.12249966361559927,0.06045737978732523,0.004194878041744232,0.004700820766935461,0.04547957400791347,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",190,18.489512000000104,-1.7611912488937378,-1.8066391944885254,0.5793691501021385,0.04036884901746118,0.11965354275889695,0.054344690669516096,0.005172267556190491,0.007332833493149036,0.04456138797104359,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.022401073947548866,3.3047096729278564,3.174509048461914,0.9850764945149422,0.007662370931908063,1.3623807542026043,0.3344507394519895,0.36279692500829697,0.15258557767544567,0.32692643883638084,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,14.738824030384421,-2.201559066772461,-2.197697877883911,0.537960970774293,0.03544865864195507,0.10509228718001395,0.11067217722249482,0.005964245647192001,0.010642156201856684,0.145203823922202,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",100,29.264816034585238,-2.3288469314575195,-2.405444622039795,0.5259642023593187,0.03545070666056835,0.1188682324718684,0.13278377388312274,0.006350290030241013,0.009682487796577382,0.14323564199730754,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",150,43.81536982697435,-2.3040831089019775,-2.366057872772217,0.524133525788784,0.02954443763704182,0.1279045082628727,0.12712674095197016,0.005730755627155304,0.009058035660943192,0.14001085911877453,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",160,46.713457841891795,-2.2928547859191895,-2.3978607654571533,0.5281685683876276,0.026853812245045128,0.10651714936830103,0.11657988755701311,0.005283385515213013,0.008416756035300723,0.13956192694604397,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,14.447387428954244,-2.0221729278564453,-2.083785057067871,0.5773635841906071,0.056209936017723255,0.07992850965820253,0.052917721136063925,0.003275986760854721,0.00552723247499913,0.13981543597765267,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",100,28.912576514529064,-2.2282066345214844,-2.3349173069000244,0.5430951397866011,0.040353554355084406,0.07774256041739136,0.03724422372398287,0.0039808489382267,0.005718461464487842,0.13889179821126163,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",150,43.40002425946295,-2.174391746520996,-2.2814736366271973,0.5363721717149019,0.044381970455849046,0.0991057810606435,0.07035234957707676,0.0038303881883621216,0.0056471410756603795,0.13641620194539428,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",160,46.28662183065899,-2.300321578979492,-2.427870273590088,0.5401902347803116,0.04514962610392663,0.11075332667678595,0.09583040546841881,0.004809089004993439,0.005957923228661482,0.14210209087468684,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,11.606645336607471,-2.1757917404174805,-2.265824317932129,0.5252645500004292,0.052733333140351835,0.06997889932245016,0.050961616406757844,0.0027197152376174927,0.00525996934375633,0.13896196382120252,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,2.9159388775005937,-1.276810646057129,-1.2755553722381592,0.6882060579955578,0.0504209728935948,0.11027051322162151,0.030055197646848518,0.0051300860941410065,0.00532135023716419,0.14592945598997176,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",70,20.300661619752645,-2.0564358234405518,-2.17645263671875,0.540094519034028,0.04410670945694568,0.09046294738072902,0.07203650269280491,0.0028102509677410126,0.0037261127521415758,0.14401409099809825,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",80,23.207683943444863,-2.216815233230591,-2.3225936889648438,0.5181595552712679,0.026970149453219853,0.08491227205377072,0.06887303044439937,0.0021883249282836914,0.003299619164796684,0.14852306386455894,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,2.897115543251857,-1.3660049438476562,-1.3519575595855713,0.633576761931181,0.03685936334973797,0.11699624126777053,0.038697267406456896,0.005278218537569046,0.004351360006400805,0.13690041308291256,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,17.373180217575282,-2.105970859527588,-2.204531669616699,0.5535572245717049,0.02941767029600011,0.07676850678399205,0.041289085869716594,0.0030472949147224426,0.004259249726577033,0.14290987513959408,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",110,31.86252916790545,-2.102350950241089,-2.2601065635681152,0.5311874598264694,0.02481057723714876,0.09668958175461739,0.0764579914754225,0.004714295268058777,0.004794808103371993,0.13969268999062479,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,8.679000892909244,-1.9749058485031128,-2.063614845275879,0.5638122409582138,0.048136648432649407,0.11152629856951535,0.10621578449585356,0.0049830712378025055,0.008457620406644885,0.14664368983358145,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,14.459190450143069,-2.1046907901763916,-2.1984550952911377,0.529661275446415,0.041948149307106046,0.09383492823690176,0.07994952423335788,0.0036652199923992157,0.006806018759311877,0.13970804191194475,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",100,28.905634766444564,-2.203523874282837,-2.252180576324463,0.5466537158936262,0.043832495029171255,0.08909724256955087,0.06961871170832289,0.00405767560005188,0.006962989840055703,0.1423277440480888,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,11.568803166039288,-1.7126600742340088,-1.8144572973251343,0.5903311911970377,0.0562330801416173,0.10442547569982708,0.043531325793775125,0.004930838942527771,0.00324841444892409,0.1423021829687059,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,14.457638437161222,-1.8398354053497314,-1.992677092552185,0.5823912471532822,0.04338372985141864,0.11013246700167656,0.06300505608725411,0.006193120032548904,0.005196237538799889,0.1480771431233734,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",100,28.903791372897103,-2.3345155715942383,-2.4513747692108154,0.5325841642916203,0.028182504780928014,0.11209890176542103,0.08421898572484843,0.003953386098146439,0.004252694625865773,0.15535135893151164,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,2.9107479015365243,-1.485608458518982,-1.4800373315811157,0.6403757706284523,0.06315262223740038,0.10035446798428893,0.04081386636472974,0.0036770813167095184,0.0043035546354342,0.1372533559333533,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",70,20.187658837996423,-2.122081995010376,-2.181443214416504,0.5338686741888523,0.043134625599386744,0.07416194723919034,0.045900137015344455,0.0026032552123069763,0.004606679875460969,0.14372295304201543,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",80,23.060507882619277,-2.103217601776123,-2.1878528594970703,0.5532182976603508,0.052751218023754896,0.0879077697172761,0.05283616626366673,0.003847707062959671,0.006924296646798595,0.13653098489157856,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",130,37.4042059618514,-2.195016384124756,-2.315481185913086,0.5559902861714363,0.047108034844783125,0.07772483781445771,0.05005067194646402,0.003216490149497986,0.005567666501170303,0.137726146960631,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,5.800114897778258,-1.2289493083953857,-1.3042107820510864,0.6564325615763664,0.04847326441307417,0.13256852515041828,0.047959651034046244,0.010081633925437927,0.010287740935398398,0.13961602514609694,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",70,20.154688813025132,-2.212596893310547,-2.2197530269622803,0.5374708473682404,0.030921897344278665,0.0871698681730777,0.059568929933171325,0.0032753311097621918,0.003744753175356662,0.13656188594177365,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,8.588786822510883,-1.8616156578063965,-1.9002861976623535,0.570459708571434,0.04008847930488537,0.09573527961038053,0.04810043163183684,0.005532078444957733,0.00697141900035719,0.13944797194562852,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,14.282633723691106,-1.9426467418670654,-2.053797960281372,0.5687491744756699,0.04231186766359939,0.0854007990565151,0.04829482360384016,0.0036178380250930786,0.0056810555127083376,0.1393477818928659,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",100,28.544423650717363,-2.128878116607666,-2.2326836585998535,0.5469916258007288,0.038449769975448245,0.09002331062220037,0.04149451289261792,0.0034283697605133057,0.002938514607924784,0.14131058100610971,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,9.209530807333067,-1.8687169551849365,-1.9938490390777588,0.5394222196191549,0.05055090932235548,0.08051154878921807,0.04961050964264966,0.0025639310479164124,0.003975724225805182,0.22365784086287022,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,13.81042824103497,-2.1980690956115723,-2.262507677078247,0.5344246048480272,0.041614784190089904,0.06225028249900788,0.034733438058396085,0.0019815638661384583,0.004171141057129658,0.22463706810958683,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",80,36.789065544260666,-2.286503791809082,-2.3117003440856934,0.5466211382299662,0.03620673671626861,0.07506462407764047,0.020533075590845624,0.002386663109064102,0.0027982217134980875,0.22306898212991655,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",140,64.3780213186983,-2.39536190032959,-2.5819878578186035,0.5156809352338314,0.022641575882915144,0.0718898440245539,0.05680041691206132,0.001394703984260559,0.0019532797578641977,0.2199109010398388,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,18.38919962896034,-2.1823322772979736,-2.3199338912963867,0.5318561010062695,0.025972634428071587,0.057643485721200705,0.02022207571664748,0.0018318966031074524,0.0029495488615121877,0.22450282610952854,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,22.975456051062793,-2.016448974609375,-1.961949110031128,0.5598733574151993,0.03209970372614253,0.07331596966832876,0.022997195349986357,0.0040544383227825165,0.006426174889570893,0.21739712404087186,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",100,46.00956566259265,-2.245471954345703,-2.494065284729004,0.5376810617744923,0.038794710870111555,0.0776871049311012,0.05463433305650892,0.002535484731197357,0.004183679845106679,0.22549428301863372,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",150,69.01028519659303,-2.3037045001983643,-2.5604050159454346,0.5181524660438299,0.02716119831366584,0.07052356691565365,0.027677384884167745,0.0018040724098682404,0.002490353260738792,0.22419107193127275,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,9.190406761597842,-1.830070972442627,-1.9529390335083008,0.6033619269728661,0.05265872538433945,0.0936040049418807,0.05243511221468046,0.005175180733203888,0.010807600712390465,0.22788621787913144,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,18.386588056804612,-2.323918104171753,-2.3982136249542236,0.5175673868507147,0.04430340045413827,0.057523520197719336,0.026313204124836797,0.0014699138700962067,0.002275942159358767,0.22291052900254726,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",90,41.39742773072794,-2.1518325805664062,-2.342825412750244,0.5196778289973736,0.03023950514215063,0.07349584810435772,0.04447478437456964,0.0021622292697429657,0.004008514871415376,0.22820290108211339,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,13.812927406979725,-1.901986002922058,-1.9727318286895752,0.5685311518609524,0.0569143181563516,0.10581486113369465,0.05600900211318792,0.0052336715161800385,0.005802217331471333,0.2373103820718825,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",80,36.809179733507335,-2.308069944381714,-2.4741129875183105,0.5336536914110184,0.02962547515203592,0.0792876083869487,0.03385089979829013,0.003314729779958725,0.004918544314015669,0.22685493482276797,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",90,41.40962967975065,-2.18780517578125,-2.317225933074951,0.5304208640009165,0.03664852200242251,0.07431772560812533,0.03983998101524592,0.003422047942876816,0.005531732712269081,0.22927625989541411,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",140,64.39554315642454,-2.2007784843444824,-2.354078769683838,0.5635933093726635,0.04453773838174274,0.10382983251474798,0.033807357607090804,0.0057050809264183044,0.0065825680761124414,0.22855681902728975,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,4.611809198511764,-1.6013370752334595,-1.5645053386688232,0.6408647634088993,0.0563219094909335,0.10396677255630493,0.03089358535587403,0.003833591938018799,0.005075485547443602,0.220133856870234,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,27.589073304086924,-2.211880683898926,-2.320828437805176,0.5386598780751228,0.03025354952367095,0.07088456733617932,0.040795102236194467,0.0020985305309295654,0.003178753775972939,0.22078917105682194,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",70,32.18333639181219,-2.0746493339538574,-2.2489407062530518,0.5414598397910595,0.03128391763466591,0.0733261404093355,0.027173225415376104,0.002086743712425232,0.001900686459425959,0.22019539517350495,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",120,55.23255421104841,-2.2773194313049316,-2.4402449131011963,0.5382322259247303,0.020721358315513392,0.08449116768315434,0.03036770986353588,0.003394573926925659,0.0031050569010556664,0.2256253131199628,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",170,78.21051867259666,-1.9670811891555786,-2.2525458335876465,0.5453133713454008,0.03666625388241074,0.09315212513320148,0.04508668573718708,0.003641802817583084,0.003350189750540945,0.22721821209415793,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,13.782850383315235,-2.138054609298706,-2.1565847396850586,0.5553531050682068,0.03878490670865628,0.07094730180688202,0.022169871037660476,0.0030681900680065155,0.006017405440339098,0.22139128809794784,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,23.017533252714202,-2.2327165603637695,-2.3280625343322754,0.5331624522805214,0.03563734053439789,0.07270873966626823,0.034775041601775594,0.002692338079214096,0.005857020522416512,0.22545987693592906,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",100,45.95749683165923,-2.2602951526641846,-2.3769099712371826,0.5327996555715799,0.0232731829180962,0.06280425889417529,0.02693675245480023,0.0016270987689495087,0.0024236154717094983,0.2265994339250028,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",150,68.91567473462783,-2.325892210006714,-2.4660158157348633,0.5373807996511459,0.022799733195683226,0.09097038698382676,0.039365889658344345,0.0044804587960243225,0.004608451605697786,0.222818735986948,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,18.401550356764346,-2.1786909103393555,-2.1966991424560547,0.5374970138072968,0.05235055712121151,0.07227281224913895,0.024796230528321646,0.004050742834806442,0.006262437407769465,0.23175662709400058,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,23.002102270023897,-2.306607246398926,-2.3462488651275635,0.5264611765742302,0.03336887899365206,0.06256127404049039,0.035868033701453285,0.001593414694070816,0.0029449616729202094,0.23932313499972224,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.0075580941047519445,3.410097599029541,3.3407516479492188,0.9884639978408813,0.0033908591962165204,1.4800301641225815,0.2993191619564446,0.3209423739463091,0.17842406198667443,0.22351172100752592,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",70,32.3201001456473,-2.200202703475952,-2.384934425354004,0.5325890053063631,0.040081932667122504,0.07706555665936321,0.042426384843812864,0.0029512010514736176,0.0034317940484069817,0.22214626311324537,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,18.275297812419012,-2.2216837406158447,-2.285231590270996,0.5365815721452236,0.04905049215476829,0.06885418528690934,0.02562199492157914,0.0032385289669036865,0.0050243256843681226,0.22063593193888664,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,22.830828892299905,-1.9478275775909424,-1.9542992115020752,0.5844974406063557,0.04895352699145485,0.08919490408152342,0.04745628099397343,0.005514807999134064,0.009328812110958342,0.2279615169391036,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,9.14913951489143,-1.9419620037078857,-2.0219204425811768,0.5472694411873817,0.048840018978198244,0.081516791600734,0.03445506845479229,0.002793543040752411,0.004477021565854498,0.23130586184561253,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_lampe[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE) [1,2].\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`lampe` package.\n    ",70,32.081455315463245,-2.1742663383483887,-2.2913765907287598,0.5603547468781471,0.040036369756504146,0.08234290010295808,0.030969743212256366,0.00309082493185997,0.002938276806901361,0.21844558883458376,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,3.7886454600375146,0.49249929189682007,0.46853452920913696,0.9144332483410835,0.018714831370628106,0.8215342648327351,0.24120048696737587,0.16615432873368263,0.16292650230893327,0.03172409301623702,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,4.9780172950122505,0.46869146823883057,0.4273509383201599,0.9146425649523735,0.019126365856767163,0.8332706689834595,0.269878951188284,0.16942748427391052,0.17272337076375424,0.0317832650616765,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",90,10.94647268904373,0.33640819787979126,0.28748607635498047,0.9094841629266739,0.020874456467013656,0.8356471061706543,0.24179571922883933,0.16589131578803062,0.16374533734369812,0.04660923290066421,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",140,16.6410062990617,0.2984044849872589,0.22679631412029266,0.9076235853135586,0.02053269230319064,0.8582489863038063,0.2554741152033147,0.17290982231497765,0.16958518009416929,0.028808290138840675,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,1.4213116860482842,0.7445068359375,0.7291824817657471,0.932405099272728,0.008593055824016255,0.8769936412572861,0.22352851456968395,0.1716855689883232,0.16314745561780042,0.030260775005444884,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",60,7.322940821992233,0.3829561173915863,0.3329087197780609,0.9087859317660332,0.017544792672817736,0.8292887918651104,0.2549821884068454,0.16375120542943478,0.16366264864626912,0.029824015917256474,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",80,9.592047634068877,0.35933226346969604,0.291183203458786,0.9110652916133404,0.019016842024690433,0.8611495643854141,0.2719029096032879,0.17613459564745426,0.17043207956024453,0.03025238192640245,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",130,15.279118998907506,0.30137383937835693,0.23187795281410217,0.9082032255828381,0.02270424156883799,0.8663652464747429,0.2952052758119709,0.17544780671596527,0.17155541327253293,0.028647524071857333,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,5.041872948873788,0.4840819239616394,0.44648057222366333,0.9145818166434765,0.017205103889718965,0.8270187340676785,0.25574711121778165,0.1683720201253891,0.17096929793648769,0.02997621288523078,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,6.156335656065494,0.40644562244415283,0.37481722235679626,0.9122323878109455,0.020157023398715408,0.8460678979754448,0.26033800859185546,0.17065577022731304,0.16895647911148107,0.030018713790923357,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",100,11.785754737909883,0.28775495290756226,0.23680976033210754,0.9082334898412228,0.02298805333224967,0.879335306584835,0.2717345227329263,0.1774502843618393,0.17470460464092724,0.030225538183003664,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,1.5854737469926476,0.794985294342041,0.7628057599067688,0.936556737869978,0.007141094060747254,0.9061094336211681,0.24038221874639829,0.17223172448575497,0.16191944843527228,0.030400488059967756,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",70,8.26937983604148,0.3751693069934845,0.3453637361526489,0.9127519577741623,0.01764372196616852,0.8510180674493313,0.26152733978353043,0.16986708529293537,0.16468654240238045,0.02966348105110228,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",80,9.251534070819616,0.3561687469482422,0.28470849990844727,0.9079250320792198,0.016120263067232028,0.873352874070406,0.2672296465906804,0.17548137344419956,0.16487494146142714,0.0295347198843956,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",130,15.139574903063476,0.3504987061023712,0.2772013247013092,0.9110115468502045,0.022430621757664505,0.8929982930421829,0.27800050950279903,0.1816322412341833,0.17179751238641625,0.03361292602494359,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.27015621308237314,1.2925325632095337,1.3196213245391846,0.9589242748916149,0.011210646595481074,1.0413885153830051,0.23785240814211173,0.1895744875073433,0.1633472360057101,0.029088130919262767,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,5.929678766056895,0.4170854985713959,0.37728625535964966,0.9106156416237354,0.019526346012423493,0.8292449563741684,0.2511629504861261,0.16613190434873104,0.16324007966430515,0.02884258795529604,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",100,11.542080646147951,0.3108770251274109,0.26081863045692444,0.9068605825304985,0.021577638738457906,0.8372048027813435,0.25409678973702043,0.16588114202022552,0.16592116156719008,0.02830725396052003,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",120,13.723347224993631,0.3890182077884674,0.2986100912094116,0.9040564373135567,0.02168385771977803,0.8653668910264969,0.24869299620949356,0.16960562951862812,0.15883322229513505,0.02832708298228681,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,3.6066640368662775,0.5696526169776917,0.5297943949699402,0.9197973944246769,0.014727213740415369,0.8178950119763613,0.25551143175536367,0.1614738330245018,0.16129500599644983,0.028378752060234547,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",80,9.12790002883412,0.334728479385376,0.2663149833679199,0.907135259360075,0.020610842350877727,0.8642867468297482,0.2643386200017754,0.17413079366087914,0.16858856608566367,0.028173777973279357,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",90,10.293295083101839,0.3503657579421997,0.26674002408981323,0.9047827757894993,0.02217764953720391,0.8414543308317661,0.2521359087913275,0.16950259171426296,0.16817269330758358,0.028292817063629627,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",140,16.009094334905967,0.29789549112319946,0.20843757688999176,0.9064913652837276,0.022420848857536115,0.8815417513251305,0.2540637442653514,0.17436626553535461,0.16473701309484953,0.027790465857833624,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,3.5608503280673176,0.4877195358276367,0.45460402965545654,0.9174180813133717,0.01636052480513274,0.8212487362325191,0.2621989701061652,0.16663138568401337,0.16833981671369147,0.028562123887240887,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",100,11.363222524058074,0.31001466512680054,0.24461513757705688,0.9065256528556347,0.021643813221584113,0.8588407412171364,0.2530983307024287,0.16669208742678165,0.16124821528017635,0.027855818159878254,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",110,12.587522763991728,0.34272322058677673,0.27336862683296204,0.9053054712712765,0.02282219013468027,0.8701909258961678,0.27272156732798103,0.1762232966721058,0.17026380691181325,0.0281293538864702,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.26769895083270967,1.3661816120147705,1.3420605659484863,0.9615475684404373,0.008144980308935547,1.0498014986515045,0.21587813683602813,0.19205920584499836,0.15896723450499936,0.028127165976911783,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,5.7580273419152945,0.4551863670349121,0.38663461804389954,0.9117772355675697,0.01885750928883491,0.8279429357498884,0.24019915830717573,0.1662837751209736,0.1656719906832739,0.028719075955450535,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",100,11.190276079112664,0.42889025807380676,0.32381710410118103,0.9092682860791683,0.02114175846503282,0.8581676259636879,0.2781942867866231,0.17386372946202755,0.1743632749560016,0.027627822943031788,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",120,13.480097641935572,0.4553343653678894,0.3664826452732086,0.9059451185166836,0.02117005963305154,0.8749807365238667,0.2712101545129086,0.17408249527215958,0.16635655283897602,0.027677861973643303,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.27042471105232835,1.4651638269424438,1.4530847072601318,0.9682932645082474,0.0051254455924008195,0.9791758246719837,0.23443178136154574,0.1798790656030178,0.1673080495085239,0.027557525783777237,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,5.7651200669351965,0.41970738768577576,0.37309467792510986,0.9121409207582474,0.022443423527441473,0.8473207578063011,0.25585777814972804,0.16877329908311367,0.16807158106087103,0.027532188221812248,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",100,11.043637668015435,0.30154359340667725,0.23381932079792023,0.906738243997097,0.022850431092706125,0.8762671314179897,0.2678307153117689,0.17686008289456367,0.17268087909218394,0.0270612312015146,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",110,12.168056786991656,0.3284381031990051,0.2591761648654938,0.9045405834913254,0.022331524233084314,0.870927732437849,0.2747155697120969,0.17263124510645866,0.16962676924987677,0.027401710161939263,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,1.3231788370758295,0.783703088760376,0.7607235312461853,0.9328608512878418,0.011333838113616183,0.8579668961465359,0.24205164974697285,0.170847337692976,0.16214029730204144,0.026316451840102673,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",80,8.905847983201966,0.3477988541126251,0.30191051959991455,0.9094205871224403,0.023062110030024417,0.8644526340067387,0.2514742338741499,0.16986547783017159,0.16438972165245275,0.027201868826523423,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,4.943479167064652,-1.1724600791931152,-1.187558889389038,0.7207868658006191,0.034083501341550866,0.16244484763592482,0.060359090363025,0.008994463831186295,0.009499047584374747,0.08839665702544153,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,7.210527667077258,-1.4068180322647095,-1.4352622032165527,0.6664479859173298,0.04494024494084782,0.1538073057308793,0.07051811439923976,0.009055718779563904,0.0067166977511037744,0.08771948190405965,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.5154175008647144,0.787810206413269,0.7631755471229553,0.9408570937812328,0.022110708262249232,0.5577845871448517,0.20284299583495752,0.1335630975663662,0.16396625633999543,0.0878238610457629,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,11.793096179142594,-1.7597978115081787,-1.7726287841796875,0.6149901561439037,0.04115811827876989,0.17786094127222896,0.13347819409344847,0.013224463909864426,0.01689982543171714,0.09001096105203032,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.5134071919601411,0.5142523646354675,0.5206994414329529,0.9153714403510094,0.04556253965154054,0.4847386721521616,0.2150888821833092,0.1065845899283886,0.14000930499499067,0.09070626413449645,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,5.090395839884877,-1.1365200281143188,-1.211035132408142,0.6970250941812992,0.0355424137433703,0.18677057046443224,0.14130547060767518,0.0117335245013237,0.021425759198892298,0.09152320702560246,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",70,16.263427959987894,-1.9032753705978394,-1.8526989221572876,0.5984510891139507,0.046248918043926175,0.11590841528959572,0.04211275739254232,0.005237191915512085,0.005286907716824256,0.08992910105735064,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,2.7574202180840075,-1.1354221105575562,-1.1541271209716797,0.7288151495158672,0.03827147295577576,0.18930966826155782,0.058687573735947345,0.013846538960933685,0.013178454357031816,0.09067974705249071,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",60,13.931634712964296,-1.6958057880401611,-1.823521375656128,0.6029055863618851,0.04275423355654545,0.13483437011018395,0.09100718183333333,0.006449602544307709,0.009565477238085396,0.08948668395169079,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",70,16.023743508849293,-1.711622953414917,-1.839666724205017,0.5893907658755779,0.0448986918670529,0.08933660527691245,0.04947657098007492,0.006013013422489166,0.012010976019798537,0.08805563603527844,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",110,24.577367281075567,-1.3681004047393799,-1.7521204948425293,0.6012524627149105,0.05098075772257905,0.10290028504095972,0.05873874260748919,0.0034287013113498688,0.004657213152459417,0.08797275484539568,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",60,13.474893127102405,-1.7441107034683228,-1.9068442583084106,0.5937508307397366,0.04298404908508149,0.1330067163798958,0.10402734982399046,0.006202932447195053,0.010390753293860825,0.08815534482710063,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,2.8434897088445723,-0.9569888114929199,-0.954483687877655,0.7478911206126213,0.0442629573948655,0.19313102029263973,0.08006060226262403,0.01541081815958023,0.019268768827071577,0.08621842111460865,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,4.875775219174102,-1.5309820175170898,-1.5699193477630615,0.6352523230016232,0.04125819791393381,0.20794255356304348,0.16877920012544292,0.015673384070396423,0.026005546060453986,0.08701427490450442,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",70,16.810424495954067,-1.6712117195129395,-1.7390497922897339,0.6330213509500027,0.04451483126911488,0.11264960165135562,0.0510477225558026,0.006916176527738571,0.010678792108064213,0.08700054907239974,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",120,26.271519797155634,-1.5127983093261719,-1.8912765979766846,0.570189107209444,0.04407332770237583,0.09571857168339193,0.07525151507820457,0.004867225885391235,0.008550171578846848,0.0860476610250771,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",160,34.52912815986201,-1.4081826210021973,-1.5894393920898438,0.573913250118494,0.040299911311573025,0.10391182755120099,0.06693700441269373,0.0039677806198596954,0.00455956620572928,0.08687145705334842,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,2.7658624418545514,-1.0631468296051025,-1.1322307586669922,0.7338878251612186,0.04700139230297799,0.16538982512429357,0.059855649095871925,0.011080577969551086,0.012504302918907516,0.08728455589152873,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",60,13.218290760181844,-1.6826130151748657,-1.686466097831726,0.6505748741328716,0.03650549494771261,0.15406292886473238,0.05647202489198596,0.010210003703832626,0.007092193837586675,0.08705712691880763,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,5.650195504073054,-1.0719878673553467,-1.1338744163513184,0.7332138866186142,0.056647055214405544,0.1610814523883164,0.08225121291544928,0.008194029331207275,0.010102224461924728,0.16094002011232078,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",70,15.490447296993807,-1.7721319198608398,-1.716836929321289,0.5992643404752016,0.05282036247550849,0.09856416075490415,0.025205191894652104,0.005143865942955017,0.008285474359951273,0.08704641903750598,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",120,26.077999957138672,-1.794698715209961,-1.9007455110549927,0.552029263228178,0.042004633251977855,0.07306010974571109,0.04727092572011829,0.0019650422036647797,0.004041451586686244,0.08619886008091271,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.49467397783882916,0.49339205026626587,0.3986080288887024,0.909940205514431,0.037593342037912925,0.459399426355958,0.17693058251786573,0.09378117509186268,0.11537391599276241,0.08763816999271512,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,11.218467616010457,-1.4856107234954834,-1.5568790435791016,0.6175512075424194,0.05081422404011589,0.10204437817446887,0.04710409779370638,0.0049123615026474,0.007296017829938898,0.08574453205801547,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.49432153603993356,0.9802152514457703,0.9796175956726074,0.9562369547784328,0.009366632054993529,0.5845034047961235,0.13867814301941286,0.13587282598018646,0.15425271657241132,0.08639082009904087,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,2.6283110899385065,-0.9505181908607483,-0.9655613303184509,0.7539015524089336,0.05476225175356347,0.1749224765226245,0.06618201241364582,0.016509514302015305,0.021411159719750054,0.08631503605283797,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",60,13.322048750007525,-1.372914433479309,-1.63728666305542,0.6245713084936142,0.04777661092159972,0.14690324594266713,0.10256294504304839,0.0089280866086483,0.014105657457217412,0.08666080702096224,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",100,22.09505316708237,-1.336611032485962,-1.9614619016647339,0.5696135722100735,0.04601373041987936,0.10911001800559461,0.08782150693801927,0.0056757740676403046,0.011465478392259074,0.08657635585404932,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",170,36.46162781515159,-1.8895987272262573,-1.912039041519165,0.60098060593009,0.06406332711603788,0.11613474250771105,0.062299694092710654,0.005635939538478851,0.0062099902453309815,0.08660526806488633,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",180,38.86732670804486,-1.4312978982925415,-1.8849430084228516,0.6197490952908993,0.02986816639577483,0.08793989103287458,0.033168084853001746,0.004432749003171921,0.006847603694043501,0.08575067017227411,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,13.145758508937433,-1.5460270643234253,-1.6365299224853516,0.6660752631723881,0.06730058595278993,0.15283032087609172,0.0700129739927482,0.010926686227321625,0.009381701809082361,0.13328929292038083,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",90,28.80840710317716,-1.9272193908691406,-1.9664297103881836,0.5640562959015369,0.0423345125419814,0.07934317411854863,0.04190663321134522,0.004265882074832916,0.006172553067179571,0.1261307040695101,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,10.066426609875634,-1.7487928867340088,-1.894803762435913,0.5783986747264862,0.046369256300307464,0.09738161298446357,0.05632015866011581,0.005271803587675095,0.0092623601797197,0.1269096308387816,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",80,25.759310964960605,-1.9943854808807373,-2.132061719894409,0.5586538389325142,0.04785607283944411,0.08619076223112643,0.062381131743450856,0.003691110759973526,0.007476786927356107,0.12772321491502225,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",100,31.93385020806454,-1.5677623748779297,-1.7784228324890137,0.6145581156015396,0.06024870827051192,0.09676139708608389,0.07532979611253693,0.004589736461639404,0.00558243543540443,0.1267649030778557,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,13.279732530005276,-1.5475085973739624,-1.6405351161956787,0.5959059186279774,0.03802149043397508,0.09822423406876624,0.036454007607331036,0.006211686879396439,0.007900902093018322,0.12717549689114094,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.6735988680738956,0.7106192111968994,0.5563062429428101,0.9311522804200649,0.029609570229715616,0.514306265860796,0.2026099505222422,0.14066355675458908,0.14370502671031982,0.13091329880990088,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,3.8816477560903877,-1.145277500152588,-1.1184303760528564,0.7133798226714134,0.051193231949486454,0.15538526652380824,0.059987050320351676,0.013518325984477997,0.014885047770412769,0.12804880598559976,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",60,19.41735970391892,-1.5784156322479248,-1.7723522186279297,0.5927399545907974,0.04419431361242249,0.09068988054059446,0.042219528232375285,0.006436612457036972,0.008507308936327396,0.1256613798905164,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,3.7369161839596927,-1.1442865133285522,-1.2656618356704712,0.6911350525915623,0.05149046370419081,0.16704080440104008,0.09022971994366427,0.010649468749761581,0.012350213788217738,0.12566548585891724,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",80,25.8505497421138,-2.1189770698547363,-2.1295011043548584,0.5740677006542683,0.045235957653829297,0.0879947142675519,0.03545937696181611,0.0036433637142181396,0.003180846869683039,0.12625688314437866,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",90,28.911212422885,-1.7302238941192627,-1.9106063842773438,0.578918170183897,0.036456169101406456,0.07717418973334134,0.02726502023541077,0.004614498466253281,0.006757719938709678,0.12576602399349213,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,7.174910895060748,-1.5299280881881714,-1.5387897491455078,0.6269210278987885,0.03568762715438431,0.1790471225976944,0.06712809147962193,0.014709878712892532,0.012275712180442619,0.12549765408039093,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",70,22.412309378851205,-1.9963878393173218,-2.10479736328125,0.5309061389416456,0.042681722593733845,0.08288017869926989,0.05288294568264245,0.0035222209990024567,0.0045280494390912175,0.12508568400517106,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",120,38.12036161497235,-1.9631547927856445,-2.148293972015381,0.5458028931170702,0.045461357877790286,0.06802586396224797,0.04273100539033833,0.00273890420794487,0.004491294723083164,0.12296042800880969,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,10.018204090883955,-1.445968747138977,-1.457493543624878,0.6400383189320564,0.08050360917923487,0.14966420037671924,0.07392299973862397,0.009744402021169662,0.008840813117053536,0.12517569889314473,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",60,19.414471983909607,-1.710044503211975,-1.8057277202606201,0.5993013195693493,0.04438767341239642,0.09060861705802381,0.03482402377742173,0.005943711847066879,0.01142244033874505,0.12516734399832785,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",110,34.935966284014285,-1.8367893695831299,-1.8880289793014526,0.6142202131450176,0.06044695826980216,0.10073772328905761,0.03648133113472789,0.007030081003904343,0.007013615848843877,0.12564135598950088,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,13.176364010898396,-1.8144850730895996,-1.884055733680725,0.5882822424173355,0.040143641848997616,0.10013918345794082,0.05017606697251399,0.006742756813764572,0.009661090504589107,0.12449850211851299,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,16.21368101402186,-1.3832041025161743,-1.73124098777771,0.5897486470639706,0.033457200471518934,0.09256678936071694,0.04343773145408836,0.00542324036359787,0.008398609332269834,0.12428565509617329,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",90,28.559007273055613,-1.982149362564087,-2.066847562789917,0.5499843489378691,0.04012164614156893,0.08561402512714267,0.03965865100488067,0.003509353846311569,0.003405259854028055,0.12498219194822013,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",160,50.42071348591708,-1.8880095481872559,-2.153027057647705,0.5371310152113438,0.03511853935197327,0.06091267103329301,0.02821876224686286,0.0016765743494033813,0.0021686675257558846,0.1239620849955827,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,13.076464431127533,-1.8988721370697021,-1.8660322427749634,0.592804878950119,0.038889915721211185,0.0992230677511543,0.06271083663433848,0.00806327536702156,0.011572793670592814,0.12342890701256692,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,16.34427752206102,-1.7406518459320068,-1.840729832649231,0.6096444986760616,0.04708939678483043,0.1144942007958889,0.05122034701700589,0.00672110915184021,0.005892291727161455,0.12332838494330645,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",100,32.22528645116836,-1.235132098197937,-1.290921688079834,0.7282951474189758,0.06366172096476472,0.10957312397658825,0.050037898031265224,0.011192411184310913,0.014833911004173143,0.12494356092065573,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,10.26547650503926,-1.6360050439834595,-1.7331554889678955,0.5986296162009239,0.03380499562441244,0.10332670575007796,0.040790486763186695,0.006093788892030716,0.006237864352587235,0.12418019399046898,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",80,26.013286110945046,-1.9103378057479858,-1.9547021389007568,0.5664067640900612,0.03686640341789324,0.10165626555681229,0.04405413466277368,0.00628063827753067,0.006707791632242526,0.12418874795548618,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=maf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",100,32.43602822488174,-2.1017556190490723,-2.1975481510162354,0.5229092389345169,0.041437445120339714,0.06410457601305097,0.04073893858425828,0.002664458006620407,0.0051655477713117605,0.12460034503601491,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7402 24-Core Processor",1008,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,2.265973787056282,0.8205182552337646,0.7774697542190552,0.934233546257019,0.008104266837584542,0.7949023135006428,0.2502856928033936,0.1586715579032898,0.15225237452928703,0.0955101118888706,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",60,11.99341180222109,0.2649901807308197,0.19992908835411072,0.9066482782363892,0.016935934385043226,0.8643549829721451,0.32953590963518214,0.16785727441310883,0.15319828499392493,0.09784012311138213,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",110,20.363285349216312,-0.2729959189891815,-0.2904288172721863,0.8918749541044235,0.01798564404765302,0.7792337499558926,0.2687528227713038,0.1650675218552351,0.15710929220158404,0.09512601513415575,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",120,21.624226892832667,-0.0016732066869735718,-0.015207042917609215,0.8958131074905396,0.017873377058683273,0.831258300691843,0.3153102002370365,0.16721612587571144,0.1543897588573279,0.09413616801612079,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,2.1536606980953366,0.8055456876754761,0.7932515144348145,0.9307498186826706,0.008825677402446802,0.7983853463083506,0.2529310601312609,0.1614695005118847,0.1573933003152349,0.09255245188251138,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",80,14.922192069003358,0.0770920068025589,0.05203311890363693,0.8970314934849739,0.01738358797396927,0.82319868914783,0.3139448634288177,0.15871529839932919,0.14383603243887255,0.09444077895022929,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",130,24.13650432904251,-0.03727872669696808,-0.019432390108704567,0.8979171365499496,0.01440488133970001,0.775547644123435,0.2426358194496426,0.1606509257107973,0.15015661955983772,0.09533252497203648,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",140,25.760670603020117,-0.02509566769003868,0.0440920852124691,0.8974563963711262,0.01322988484277667,0.8354374505579472,0.3193929602890722,0.16401183977723122,0.14814405163471606,0.09533374989405274,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,5.808656743960455,0.23287568986415863,0.1991731822490692,0.901119027286768,0.01568383200416914,0.8315055668354034,0.2785864113337369,0.16502558253705502,0.1525107739146088,0.09455747599713504,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",80,14.542848394950852,0.1482846885919571,0.04810764640569687,0.9012121371924877,0.015083544321221677,0.8333727903664112,0.2670243053479585,0.16955487243831158,0.15419180323103152,0.09456897294148803,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,3.872865069890395,0.38719645142555237,0.3420417606830597,0.9063129834830761,0.018708975869496214,0.7902810610830784,0.2559053420111396,0.15983488969504833,0.1518852285974903,0.09385539614595473,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,9.20725452201441,0.11364728957414627,0.1160731241106987,0.9022480472922325,0.01592456379726917,0.8788738623261452,0.35411439862082433,0.1641355473548174,0.14676726975670543,0.09383542789146304,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",100,18.062900636112317,0.04853123053908348,0.0590956024825573,0.903136819601059,0.012193547317179119,0.8625069092959166,0.37410697007750193,0.16634375415742397,0.14897306603531754,0.09313201485201716,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.39659102796576917,1.8912749290466309,1.89146089553833,0.9718025736510754,0.007596431273947727,0.9683597087860107,0.1759064739780715,0.18621452897787094,0.1682693485727125,0.09342257003299892,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,9.31489494186826,0.27935701608657837,0.31193867325782776,0.9037195108830929,0.01588455656541413,0.8951071873307228,0.3344931392419734,0.16831637360155582,0.1466237510440307,0.09397290903143585,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",100,18.259699888061732,0.23502635955810547,0.18289294838905334,0.9025546349585056,0.015069030360874975,0.8795187473297119,0.35565441332314546,0.16874265670776367,0.14750659664511018,0.09381715697236359,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",110,19.839377496158704,0.06535480916500092,0.030129658058285713,0.8977016396820545,0.014336792750574109,0.8416297137737274,0.2779540301196609,0.1681611929088831,0.1469885770217286,0.09371599112637341,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",120,21.896908813156188,0.09488777816295624,0.07306784391403198,0.9039581976830959,0.014343724266247835,0.8402278330177069,0.2843049990899401,0.16672915406525135,0.1543145622596759,0.10105465794913471,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.3960060738027096,1.3548945188522339,1.3333103656768799,0.9558409117162228,0.011836107769581931,0.8440992906689644,0.2156063785233072,0.16232417151331902,0.15833299938937584,0.09335063397884369,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,2.142590590985492,0.6434562802314758,0.6098099946975708,0.9266978427767754,0.010984625505425403,0.8311704508960247,0.26668513955980644,0.16366875357925892,0.15832281752845953,0.09479324403218925,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,4.021376237040386,0.9449944496154785,0.8916286826133728,0.9272133074700832,0.02119781613455652,0.7869002763181925,0.23310127506547795,0.15716557949781418,0.14970256976478588,0.09445773693732917,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,5.637570567894727,0.39473503828048706,0.3747153878211975,0.912294115871191,0.012867561294308678,0.835611866787076,0.3223387932032475,0.15699708089232445,0.14761733029462204,0.09357156604528427,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,7.527994335861877,0.2027798891067505,0.18274511396884918,0.9037446342408657,0.011116391781427603,0.8890864849090576,0.34248721386671754,0.16817796230316162,0.14886392134835477,0.09406455117277801,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,9.25572636607103,0.18281619250774384,0.14410047233104706,0.9009995423257351,0.01480564435428363,0.8162403702735901,0.26599812320294547,0.15927991829812527,0.1431057343872286,0.09408567100763321,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",60,10.79990900401026,0.1754290610551834,0.18507178127765656,0.8985289111733437,0.012527776818032535,0.8004156965762377,0.2512129971983208,0.15547268092632294,0.14047314061709212,0.09350572107359767,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",70,12.811396992998198,0.11235344409942627,0.06161723658442497,0.8949862830340862,0.018383834526606736,0.8853270467370749,0.3608723127694621,0.1675571519881487,0.14588392956701768,0.0944445370696485,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",80,14.498161833034828,0.18144792318344116,0.16385605931282043,0.9010324142873287,0.010973274008753994,0.8395808357745409,0.27021127115807386,0.1649655718356371,0.14634293506213122,0.0939228420611471,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",90,16.17539306706749,0.03952153027057648,0.0312349833548069,0.9013949818909168,0.016253875297840965,0.8794601783156395,0.32079256640496934,0.17007112130522728,0.14880008233115183,0.09362376993522048,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",100,17.982726092915982,0.05200383439660072,0.07657594978809357,0.8975212462246418,0.01659325229787503,0.935393676161766,0.37602146272108317,0.16458525136113167,0.13350225728872023,0.09252814389765263,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",110,19.697732751024887,-0.061904579401016235,-0.12423036247491837,0.8970353752374649,0.016769138857576204,0.8795874267816544,0.30828715301996623,0.17282142117619514,0.14695855466816032,0.09284648904576898,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",120,21.52591656614095,0.0970400720834732,0.06947341561317444,0.900850385427475,0.011865260781627538,0.8211191538721323,0.2748942084712508,0.1622904483228922,0.14753221189671265,0.09315299498848617,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",130,23.28352506295778,-0.05515917390584946,-0.07448103278875351,0.8964217454195023,0.017531801828452934,0.8982366509735584,0.37659537898092715,0.17133983969688416,0.14814539053975878,0.0933616270776838,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",140,25.250413936097175,-0.12651097774505615,-0.1555926501750946,0.8967618048191071,0.015368083968315684,0.8774488195776939,0.37842646712239864,0.14559375308454037,0.13699908334486283,0.10015863087028265,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",150,27.064675288042054,-0.13424375653266907,-0.16101235151290894,0.8937383703887463,0.013676871036262379,0.8311579842120409,0.30217643362124497,0.17210832983255386,0.15586938579453793,0.09385613701306283,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",160,28.735237485030666,-0.12843972444534302,-0.0763433650135994,0.8921811729669571,0.014780036580431156,1.0218206383287907,0.3336331887511037,0.1804117877036333,0.14776046604559723,0.09277173317968845,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",170,30.626821185927838,-0.19132858514785767,-0.20402207970619202,0.8933405354619026,0.016936191484677995,0.775707557797432,0.2602758889594335,0.16377714090049267,0.15106467598446172,0.09339179890230298,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",180,32.29810345196165,-0.22772887349128723,-0.32033824920654297,0.8955364860594273,0.014822958110724925,0.7882916200906038,0.25613021253700735,0.15992476977407932,0.14735453219863903,0.0937890550121665,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",190,33.820780689129606,-0.21824567019939423,-0.23938360810279846,0.8910227902233601,0.014702514875265645,0.829418221488595,0.3165773622221574,0.16702299006283283,0.15087635510410724,0.09382428112439811,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",200,35.939824999077246,0.026788780465722084,-0.001744571141898632,0.8930338621139526,0.01480823856085427,1.0271352380514145,0.32186084125093584,0.1798462960869074,0.15028639646344324,0.09353025886230171,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",210,38.00346376514062,-0.07374656945466995,-0.05926045402884483,0.8965127654373646,0.01186256530635296,0.8427479267120361,0.3333538350825561,0.16168534383177757,0.14362409287433162,0.09285402786917984,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.3866165049839765,1.3749773502349854,1.3483517169952393,0.9576396159827709,0.009197827148970905,0.8830935545265675,0.2503045379639879,0.17602789029479027,0.16989145040838163,0.09452571696601808,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,2.1115180468186736,0.8222672343254089,0.7672809362411499,0.9328009970486164,0.012631024929751301,0.8114817440509796,0.24782770036608698,0.16608358547091484,0.1583349816351945,0.09402814693748951,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,3.9665913640055805,0.4264655113220215,0.40721651911735535,0.9069816395640373,0.01622767656144806,0.8482299335300922,0.3369263432463282,0.1579588633030653,0.14602363604912025,0.09402437508106232,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,5.548802032833919,0.29958364367485046,0.22803480923175812,0.9021012745797634,0.017212482227975633,0.8040206171572208,0.26881336632903313,0.16327960044145584,0.1552372934210525,0.09202149510383606,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,7.415830384008586,0.2804579734802246,0.3120516240596771,0.9024943336844444,0.017195867260649502,0.8522940129041672,0.265381210096993,0.16624273173511028,0.14843162654537323,0.09331547585316002,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,8.984687560005113,0.2948399782180786,0.2864741384983063,0.9021297506988049,0.014362259087587398,0.8512542806565762,0.295433347006517,0.16279872506856918,0.14424502838245823,0.09261444606818259,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",60,11.041812857147306,0.13919606804847717,0.1379268765449524,0.89874367415905,0.014269830026181539,0.806382842361927,0.2566800497061445,0.16243026964366436,0.14559225196426337,0.09348794515244663,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",70,12.51802275213413,0.18679054081439972,0.14408837258815765,0.9003285057842731,0.019630531999280266,0.8711367696523666,0.3466763977953333,0.16555624268949032,0.1489746535196381,0.09236079198308289,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",80,14.39628737512976,0.16979601979255676,0.16388773918151855,0.8972444646060467,0.011827196216738523,0.8371979631483555,0.31341974278715423,0.16125802882015705,0.14069119508550393,0.0941892119590193,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",90,16.154402231099084,0.0243833027780056,-0.008330803364515305,0.89886374771595,0.013431723517351724,0.9233042746782303,0.39765480944359666,0.1716719325631857,0.14772745587096536,0.09263187786564231,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",100,18.17553702997975,-0.08039447665214539,-0.0847373902797699,0.8937963582575321,0.01598749501905157,0.882348170503974,0.35495412011912886,0.16712341830134392,0.14686428052167486,0.0929843969643116,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",110,19.74521799199283,-0.016407420858740807,-0.009300729259848595,0.8994723111391068,0.014976918261234411,0.9064791575074196,0.41071328425691184,0.16951725259423256,0.14790622312501794,0.0932185270357877,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",120,21.494167571188882,0.01380056980997324,0.01788433827459812,0.900173831731081,0.020729152016447962,0.8148181848227978,0.25604401336306565,0.16745462082326412,0.15508691000968639,0.09334068302996457,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",130,23.440504959085956,-0.08249916881322861,-0.06376080960035324,0.8985313661396503,0.016073004888335057,0.8573744222521782,0.34940325383848464,0.17085847817361355,0.1555135379734712,0.0926103659439832,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",140,25.417879472021013,-0.1849401444196701,-0.172132208943367,0.8921188563108444,0.017410216238919272,0.8878921642899513,0.36358751397734107,0.1720216777175665,0.1512085819555797,0.09322314593009651,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",150,26.947867530863732,0.139242485165596,0.1274406909942627,0.897827759385109,0.01498793435729137,0.8900597915053368,0.3303507365931796,0.17302104830741882,0.1501318147170319,0.09225752297788858,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",160,28.740583637030795,0.04819619283080101,-0.003100175876170397,0.8957524262368679,0.016283011826015728,0.8357153348624706,0.29506624820628424,0.15404517203569412,0.14038111098408804,0.09332129079848528,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",170,30.3547969579231,0.06138411536812782,-0.0009886878542602062,0.9019493535161018,0.013180348115486441,1.4229904673993587,0.8414010347048194,0.18348813243210316,0.1383760771102096,0.0936306060757488,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.3869147449731827,1.2766519784927368,1.2625821828842163,0.9557480327785015,0.010059222227670613,0.875019446015358,0.24945408979623,0.17491726204752922,0.17125506863178275,0.09383573499508202,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,2.081834948156029,0.6286106109619141,0.5751827955245972,0.9198612682521343,0.01652192922057797,0.7867271788418293,0.2399531876850524,0.155255988240242,0.14915416258686445,0.09330065594986081,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,3.881138233933598,0.544989824295044,0.5389705300331116,0.9239808544516563,0.011027117539855438,0.800684779882431,0.26536934149666,0.15689205937087536,0.14871052797735,0.09052689489908516,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,5.44221162982285,0.424996018409729,0.38019439578056335,0.9094857983291149,0.017284446887509658,0.7773642409592867,0.24317163600051775,0.15617984533309937,0.14548334588692033,0.09185493807308376,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,7.109215571079403,0.4215768575668335,0.3939550817012787,0.9068606533110142,0.013147745325940936,0.8063144516199827,0.26470879788886786,0.159370394423604,0.1447943975949977,0.0907226549461484,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,9.027530922787264,0.4380846917629242,0.41189247369766235,0.907314095646143,0.01620845839078891,0.8522739391773939,0.3253423113219755,0.16715329326689243,0.14772807482899883,0.08958068978972733,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",60,10.490873325848952,0.5413244962692261,0.4807661175727844,0.9122605733573437,0.012070750683930303,0.8457126505672932,0.31682489712642903,0.1687988843768835,0.149040313475144,0.0914998350199312,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",70,12.34975751885213,0.16812357306480408,0.12846563756465912,0.8973117806017399,0.014915835443523533,0.8834691941738129,0.2588831103257571,0.17282603308558464,0.14268454545352718,0.09028354496695101,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",80,17.559071174124256,0.04431535303592682,0.04484303295612335,0.8961203657090664,0.017909512565008893,0.8284304924309254,0.26941072841907976,0.16029534116387367,0.14199075008722786,0.1814566319808364,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",90,15.898458941141143,0.02781078964471817,0.015764687210321426,0.8904415927827358,0.017078004461830543,0.8273552544414997,0.2888164000713569,0.16532291285693645,0.1440903865960881,0.09033997915685177,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",100,17.702864272985607,0.2001076489686966,0.20202279090881348,0.9104285426437855,0.013649011057300542,0.8682487867772579,0.37235183830359236,0.16095799766480923,0.1432621529831329,0.08947625895962119,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",110,18.723309694090858,-0.013671699911355972,-0.01599724218249321,0.8951125591993332,0.017197060877705863,0.8653762377798557,0.34251175526079963,0.16789585538208485,0.14928875307615996,0.09008014993742108,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",120,20.679975799052045,0.16911789774894714,0.14794126152992249,0.9015501849353313,0.017376398834796504,0.8739811554551125,0.3345469158648865,0.16211456060409546,0.14460159028643163,0.08935178210958838,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",130,22.285436986014247,0.007056753151118755,-0.0321069210767746,0.8970936685800552,0.015898067357428772,0.9276019744575024,0.28707622059594323,0.16900838539004326,0.14652987933400596,0.08908722293563187,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",140,23.676509300014004,0.2658180296421051,0.3452765643596649,0.909146636724472,0.015296102648292759,0.802633848041296,0.24728767954591277,0.16516982577741146,0.14946504810058905,0.08464650087989867,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.37558478489518166,1.5095007419586182,1.5540705919265747,0.9555952996015549,0.007266904617923171,0.8939156122505665,0.23798522222883897,0.17894267477095127,0.16651587660347275,0.0853047058917582,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,2.0180854201316833,0.9993497133255005,1.0363646745681763,0.9342653788626194,0.008385137387760339,0.7891194578260183,0.2543853360424392,0.1587982028722763,0.15532396518894026,0.08425299287773669,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,3.8064993279986084,0.5386847853660583,0.45475226640701294,0.9081702157855034,0.013287877439225541,0.7961879689246416,0.24603698844575747,0.16187534295022488,0.15250370868389185,0.08447712892666459,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,5.306805260013789,0.21107979118824005,0.19354763627052307,0.9000219143927097,0.018135964855852327,0.8353153727948666,0.26492792849647,0.16213078424334526,0.1482781069032794,0.08492038794793189,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,7.1118304340634495,0.2795506417751312,0.2995254695415497,0.9067992307245731,0.011727643974899556,0.8276070263236761,0.28575414432666224,0.16601425223052502,0.1490208726275175,0.0844779359176755,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,8.68528745090589,0.19251945614814758,0.1523759365081787,0.8994107432663441,0.017508037333290486,0.875672247260809,0.31456098267551297,0.17097930796444416,0.14652387300688152,0.08337948401458561,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",60,10.2635072178673,0.17862288653850555,0.12328340113162994,0.8970327712595463,0.018149303134743573,0.8575085662305355,0.31932690512113443,0.16439264453947544,0.14780022438899237,0.08266535610891879,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",70,12.025530555984005,0.14436355233192444,0.12478731572628021,0.9010921977460384,0.019435787009881012,0.8760525789111853,0.3701488053151392,0.16858408600091934,0.15371329710749004,0.08283953694626689,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",80,13.570013680960983,0.3097730576992035,0.3015685975551605,0.8991388864815235,0.015143975007446844,0.8415867239236832,0.2888600962061284,0.16639851592481136,0.14794685345391784,0.08340398385189474,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",90,15.380010972032323,0.5276947617530823,0.43168237805366516,0.9056694619357586,0.017444479891892968,0.9011371731758118,0.36101431616792207,0.1673548985272646,0.1466581608090654,0.08311403403058648,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",100,17.22802745993249,-0.1493341624736786,-0.13412654399871826,0.8918429166078568,0.017755601902164284,0.8415684774518013,0.27425705628428554,0.16687889397144318,0.1457334196545081,0.08367395191453397,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",110,18.55053106485866,-0.07082020491361618,-0.09161149710416794,0.8966357484459877,0.014323780332250718,0.9026907440274954,0.3863736614958799,0.16975019685924053,0.1484810169308237,0.08297196100465953,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",120,20.463995297905058,0.07023277878761292,0.09050239622592926,0.9008772969245911,0.012018911439597685,0.8377051502466202,0.30514184890814766,0.16568636149168015,0.1504087910046634,0.08206360600888729,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",130,22.202857743017375,-0.054752569645643234,-0.10636721551418304,0.8917220719158649,0.018332664495649287,0.8256457056850195,0.2942220150679487,0.16611088067293167,0.1476680508306925,0.08277838909998536,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.3889071720186621,1.1375395059585571,1.1442220211029053,0.9501660168170929,0.007461490495429439,0.8740783147513866,0.2502635868592587,0.16853679902851582,0.16212135076820244,0.08271249593235552,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,2.011932753957808,0.8301072716712952,0.7272653579711914,0.9271857962012291,0.009792530293853251,0.7994520701467991,0.2368644047740734,0.15967276692390442,0.15292657017703173,0.08207473205402493,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,3.777625262970105,0.2832389771938324,0.2798363268375397,0.9082627184689045,0.016131939827614454,0.8129172157496214,0.2709985477199583,0.16163653507828712,0.1562988109651999,0.08237954298965633,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,5.278207608032972,0.25108271837234497,0.2445773482322693,0.8968801833689213,0.015266714019741633,0.8809881061315536,0.2578484254966315,0.16824211552739143,0.14499048480475304,0.08214992308057845,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,7.039687562966719,0.1349632441997528,0.11889494210481644,0.8998090140521526,0.013698817938953485,0.8431517779827118,0.2931275293566537,0.15929665602743626,0.14085937814001587,0.08202864299528301,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,8.699955522781238,0.10468938201665878,0.08124462515115738,0.900266632437706,0.015800893900323614,0.8465326633304358,0.33947077096826356,0.16366766393184662,0.1526579051748152,0.08217069203965366,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",60,10.197886985028163,0.01619781367480755,0.014854677021503448,0.8994155898690224,0.013720558410902608,0.8851163368672132,0.3631430827788226,0.17009512335062027,0.1505181387023489,0.08341672504320741,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",70,11.97154034115374,0.12589576840400696,0.106119304895401,0.8938584513962269,0.017290732291560272,0.8826490603387356,0.2868374855230211,0.16838374361395836,0.1458366399470603,0.0828707639593631,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",80,13.838697209954262,0.3022664785385132,0.29782894253730774,0.9039265923202038,0.01471482841837597,0.8432893473654985,0.30961456582871105,0.16385797038674355,0.145083898273908,0.08238799194805324,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=1]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",90,15.471457430860028,0.03962006792426109,0.03175880014896393,0.8965747803449631,0.016871432578810536,0.8945062346756458,0.37889068913179125,0.16843700408935547,0.14614190117036424,0.08221432799473405,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,1.2014303340110928,0.2812875807285309,0.2889137864112854,0.8892218470573425,0.04153776451231721,0.45423915050923824,0.1528570260719605,0.06878839433193207,0.06740580277837507,0.44002670189365745,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,5.296866081189364,-0.9857897162437439,-1.0794557332992554,0.7041903994977474,0.056379190400470354,0.12278622761368752,0.03263742985615285,0.008780665695667267,0.012547750248232967,0.29863866209052503,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,9.547256191028282,-1.1139752864837646,-1.143172264099121,0.700036596506834,0.06094688891727161,0.12103581940755248,0.06525076296425511,0.005543150007724762,0.007743355488463847,0.2974781880620867,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,14.07707070815377,-1.2285997867584229,-1.3134703636169434,0.6684647276997566,0.04959113261122621,0.1107436278834939,0.04674394163955269,0.004984714090824127,0.005126835475048067,0.29755731602199376,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,18.685872267000377,-1.7854818105697632,-1.8213235139846802,0.5937148965895176,0.06269960106213485,0.0842690821737051,0.03198560375523441,0.004923876374959946,0.01054393577458984,0.2996967132203281,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,23.61927533103153,-1.5988787412643433,-1.6484241485595703,0.6615568250417709,0.08650768693804413,0.08535111835226417,0.03693908555028422,0.002657845616340637,0.004058243766925799,0.30097982101142406,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",60,27.561286797048524,-1.872053861618042,-1.9182790517807007,0.6097403690218925,0.048363510072504244,0.1108309046830982,0.07313457865559066,0.005487468093633652,0.008188443873789548,0.3011552810203284,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",70,30.796494988026097,-1.925719976425171,-1.930463433265686,0.6037910655140877,0.054907424903644725,0.08264702255837619,0.03616233513394937,0.003677085041999817,0.007605686879840658,0.2887378630694002,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",80,35.180054201046005,-1.771508812904358,-1.8238469362258911,0.6078159138560295,0.0549541569046131,0.08476152503862977,0.044752430226779806,0.006090275943279266,0.010858043480194833,0.2999517400749028,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",90,39.15750080207363,-1.9640343189239502,-1.941420555114746,0.5611888729035854,0.042758045516900135,0.06971540208905935,0.04170466982252459,0.0032514967024326324,0.007433601407606545,0.29063266795128584,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",100,43.16055322997272,-2.097733497619629,-2.1981253623962402,0.5459803845733404,0.04699736286296924,0.08294010045938194,0.05436392782668947,0.00260070338845253,0.004555528191439157,0.28234801907092333,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",110,46.51724181184545,-1.7710490226745605,-1.8401365280151367,0.583376694470644,0.04384875162848939,0.0921441288664937,0.04915772540865233,0.0035259872674942017,0.004887358059230221,0.30377291119657457,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",120,51.57165938592516,-2.1224093437194824,-2.2378082275390625,0.5257869455963373,0.03089518529889449,0.07466056500561535,0.03591349923752769,0.0029001086950302124,0.004750801176146902,0.35839737695641816,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",130,60.741853300947696,-1.7466161251068115,-1.814192533493042,0.5712911412119865,0.054006392339220284,0.09092418826185167,0.056195548797482626,0.0027032531797885895,0.004028643810329606,0.288390836911276,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",140,59.7126855829265,-1.8372745513916016,-1.9665985107421875,0.5469317678362131,0.06038406986086189,0.06774946756195277,0.04284994565433,0.0023397505283355713,0.0052609949078720455,0.2875553679186851,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",150,63.20990480110049,-2.116105794906616,-2.192373752593994,0.5268776379525661,0.0431416474787872,0.07860995968803763,0.06615330676262761,0.0028395168483257294,0.005501205067932379,0.2875760830938816,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.9008130521979183,0.26060160994529724,0.188405379652977,0.8968495465815067,0.03914313916196687,0.3952800529077649,0.11156025735982512,0.0485660545527935,0.05009042839537815,0.28984871809370816,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,5.03137742402032,-1.0688291788101196,-1.1161448955535889,0.7037587203085423,0.06403735408928143,0.13986374577507377,0.04213497540289784,0.008049260824918747,0.008873193494876263,0.28872421593405306,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,9.322719804942608,-1.3193778991699219,-1.4011311531066895,0.7016288973391056,0.07199759038808931,0.11847912007942796,0.041253170085001185,0.006674032658338547,0.009740200118812077,0.2896588291041553,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,13.339030742878094,-1.5159531831741333,-1.7041573524475098,0.6332097165286541,0.05680543344878818,0.09547959361225367,0.029817443720756544,0.004895731806755066,0.00792012512712801,0.2890152030158788,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,17.776919816154987,-1.5044134855270386,-1.6166150569915771,0.5989420861005783,0.06308931381431435,0.09197300369851291,0.045992797820070395,0.0017169192433357239,0.0021006619496311365,0.28804858098737895,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,22.168809592956677,-1.6241891384124756,-1.704610824584961,0.6588415428996086,0.03867974655702292,0.1388744763098657,0.07342430837029823,0.009681813418865204,0.015075337824744593,0.28873293404467404,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",60,25.94939123489894,-2.042454957962036,-2.050004005432129,0.5860865265130997,0.05705814887042983,0.07430806709453464,0.03676712526288729,0.003402993083000183,0.006767718864254392,0.2900967139285058,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",70,30.447345745982602,-2.0778021812438965,-2.129922866821289,0.5351839680224657,0.04089828778605697,0.06693210336379707,0.031772649874759105,0.0026301220059394836,0.004587166683336507,0.28779102116823196,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",80,34.34059489914216,-1.97003972530365,-2.004940986633301,0.5510194357484579,0.05208467953300489,0.09173636510968208,0.0623223995043366,0.004208344966173172,0.008588305365885058,0.2870305071119219,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",90,39.09692706586793,-1.9171476364135742,-1.941236972808838,0.5591722801327705,0.04050725362768994,0.08250538073480129,0.047532255439698794,0.0050568655133247375,0.010818443399332507,0.2877675008494407,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",100,43.47266084398143,-2.0598349571228027,-2.163195848464966,0.5400989167392254,0.03858683186550231,0.08749975776299834,0.05187129499712673,0.004101987928152084,0.006457050336055808,0.29092021682299674,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.8865242199972272,0.16893520951271057,0.14513787627220154,0.8566593825817108,0.05247797105434456,0.3628781018778682,0.13308308259138982,0.04068172723054886,0.046051178018028265,0.28990550921298563,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,5.021701094927266,-1.311710238456726,-1.3544840812683105,0.6847826354205608,0.05011625391937412,0.11942158639431,0.03795265532745687,0.006259690970182419,0.00933940293132312,0.28919569798745215,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,9.186048608040437,-1.801166296005249,-1.7587389945983887,0.6036620326340199,0.05713076738267859,0.09381650551222265,0.03699442822098456,0.0036985352635383606,0.005648780538374367,0.29055250296369195,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,13.519632757175714,-1.5879340171813965,-1.6420564651489258,0.633079644292593,0.03993629175625494,0.12731938436627388,0.05591156938924063,0.006762746721506119,0.0075118953989129844,0.288532234961167,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,17.610856869025156,-1.7317931652069092,-1.8053463697433472,0.609858438372612,0.043555218503388116,0.11066057183779776,0.03949995579560389,0.0040370747447013855,0.003649696887041439,0.28956312499940395,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,21.6716358368285,-1.568449854850769,-1.5049833059310913,0.6607655435800552,0.040783933391199956,0.10052743926644325,0.05939018673250638,0.004061110317707062,0.006547147747590441,0.2899542700033635,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,1.0483681089244783,0.33735018968582153,0.30916666984558105,0.9039887562394142,0.03883862996017362,0.42210645880550146,0.12987634614031565,0.07073923386633396,0.08515553611899408,0.2893095319159329,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,5.0438726199790835,-1.121309757232666,-1.2038862705230713,0.6767547279596329,0.06330682927371867,0.13581562507897615,0.032309559536915965,0.010919898748397827,0.016619407282836944,0.2844900090713054,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,9.170058976858854,-1.3374557495117188,-1.3866196870803833,0.6550008244812489,0.06415772317815899,0.11492673587054014,0.04001189756334801,0.005792506039142609,0.009127631756346006,0.2889342359267175,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,13.278473301092163,-1.6751534938812256,-1.6762367486953735,0.614617146551609,0.04273314044651658,0.13366558705456555,0.07585217534358116,0.007476400583982468,0.01001763791323698,0.2876465590670705,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,17.641947417985648,-1.6334381103515625,-1.677524209022522,0.6237717010080814,0.05688221586963102,0.10706399870105088,0.04843959721035961,0.006744384765625,0.008758210954453269,0.2871034140698612,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,21.55290206684731,-1.780844807624817,-1.8497246503829956,0.5919832773506641,0.05344101740030157,0.07939604064449668,0.0445213630615947,0.003507617861032486,0.006122025002604439,0.28643325506709516,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",60,26.034820060012862,-1.7887213230133057,-1.7467126846313477,0.5728681609034538,0.05799638376540689,0.08066356088966131,0.05127009756043349,0.0033422857522964478,0.006420358610826669,0.2894641801249236,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",70,30.947439906885847,-1.7572301626205444,-1.7642879486083984,0.6380966380238533,0.05635355606583096,0.09677442419342697,0.037450726734518804,0.004027996212244034,0.005982192109852334,0.2886303630657494,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",80,34.955497388960794,-1.7887150049209595,-1.912290096282959,0.5870963502675295,0.06305122348817382,0.08714336901903152,0.05286200957014372,0.002026304602622986,0.0036079144805921197,0.2912981219124049,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",90,37.941125905839726,-1.4499962329864502,-1.5578653812408447,0.6461165696382523,0.05650641309462657,0.11086256802082062,0.07633323243201577,0.0048844777047634125,0.007810465236202154,0.2863788998220116,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.8802959511522204,0.010933688841760159,-0.04298274219036102,0.8775354847311974,0.06707072510038466,0.339109443128109,0.07188187676339977,0.040907103568315506,0.051806541939438476,0.2877855298575014,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,4.981418180977926,-1.0905451774597168,-1.1661897897720337,0.7262516357004642,0.042233602918994424,0.1411941908299923,0.031897635872580106,0.011058203876018524,0.015779910393164214,0.28785614017397165,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,9.140694421948865,-1.2987644672393799,-1.289931297302246,0.6859124712646008,0.07363006244243939,0.12045984016731381,0.05305957437595778,0.007138118147850037,0.016436928240407375,0.28668852406553924,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,13.367236512945965,-1.7142802476882935,-1.7182224988937378,0.6194091029465199,0.04860534589937171,0.08583120093680918,0.02703833713847855,0.004050206393003464,0.006635014857173448,0.28840145003050566,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,17.56181417987682,-1.7956414222717285,-1.8155639171600342,0.5690552480518818,0.03863333406146186,0.08335809572599828,0.033708766210739226,0.0027659647166728973,0.00417138204401224,0.2872625170275569,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,21.894249914912507,-2.0204286575317383,-2.037424325942993,0.5638062097132206,0.05778148001448169,0.08482259977608919,0.040789847705131776,0.0031550638377666473,0.0050533315068690176,0.2875315479468554,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",60,26.070922350976616,-2.0689544677734375,-2.063420534133911,0.5474176369607449,0.04395326118410695,0.08747430541552603,0.047282171579996944,0.0035753026604652405,0.0046758442877184525,0.2876054949592799,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",70,30.432776828994974,-1.9943888187408447,-2.0728819370269775,0.5459517575800419,0.03539382993895866,0.06457182043232024,0.02929702365877936,0.0018443502485752106,0.0036171854573581507,0.2823410660494119,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",80,34.784586193971336,-1.7953753471374512,-1.819324016571045,0.609557967633009,0.05902122236465298,0.07874252437613904,0.032152227620345884,0.0024730227887630463,0.005153451321641236,0.2890314790420234,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",90,39.0064413510263,-1.8480310440063477,-1.9263750314712524,0.6071425657719374,0.05622713526555255,0.07725622714497149,0.03316605857005517,0.002529710531234741,0.005650063657378593,0.2879593828693032,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.8885073389392346,0.24762821197509766,0.24039608240127563,0.8946245983242989,0.047372348892707236,0.41471605747938156,0.10426938669204852,0.05470207333564758,0.06294651409216391,0.28857502480968833,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,4.980048690922558,-1.3949095010757446,-1.401733636856079,0.662108201533556,0.044955821687332156,0.11699221702292562,0.033354519585427365,0.006207369267940521,0.008285894278251877,0.2882635591086,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,9.210863742046058,-1.1650118827819824,-1.3628803491592407,0.6651591546833515,0.05755708676775773,0.12453903118148446,0.0503926023816727,0.01033327728509903,0.021456225913102932,0.28960862592794,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,13.210589308990166,-0.8863202333450317,-0.9890329241752625,0.6842269226908684,0.06619862366931763,0.15232013957574964,0.10341297044534399,0.012042243033647537,0.01730321363790388,0.28636103589087725,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,17.42246770206839,-1.802910327911377,-1.8197109699249268,0.5730200055986643,0.05476849821068318,0.08371561160311103,0.05068981310468806,0.004775427281856537,0.009328125315682511,0.2869295480195433,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,21.780089414911345,-1.501962661743164,-1.6280231475830078,0.5979591757059097,0.05100867298660436,0.08444339828565717,0.033117378332892584,0.005397431552410126,0.008562059777482383,0.2861123359762132,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",60,25.702908175066113,-1.887036681175232,-1.9257118701934814,0.5773903448134661,0.04643309564731937,0.0881307004019618,0.045465188723728914,0.004025660455226898,0.007351413158575217,0.28992218105122447,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",70,30.488129508914426,-1.7409344911575317,-1.7442269325256348,0.6296639703214169,0.06990194379996316,0.07864802004769444,0.04033274790142381,0.003588717430830002,0.006750446333541169,0.2889308671001345,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",80,34.42644651606679,-1.8430538177490234,-1.9421252012252808,0.5593809280544519,0.038773835307769906,0.07308561448007822,0.026711654102249217,0.0019668973982334137,0.0027244456439258174,0.29004062595777214,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",90,38.69500221405178,-1.8668675422668457,-1.9798778295516968,0.576537137851119,0.0725783582765637,0.07005228253547102,0.049508314985547155,0.0027945861220359802,0.006096707836404433,0.2873491330537945,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.8818640138488263,0.11674743890762329,0.04312621057033539,0.891052458435297,0.03524716875495785,0.36267776787281036,0.06488846989808657,0.04728097468614578,0.06099550203017555,0.2882759040221572,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,5.141391105018556,0.5686123967170715,0.4713842570781708,0.842876959592104,0.05895804728900646,0.22386083006858826,0.05743295957790622,0.026415694504976273,0.02751664831294781,0.29433325002901256,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,9.083251800155267,-1.4817603826522827,-1.4077764749526978,0.6513645723462105,0.04451500743892728,0.104295470751822,0.04664543328988565,0.00778043270111084,0.012744461284326408,0.28741616010665894,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,13.433394604129717,-1.5819200277328491,-1.598981499671936,0.6245418637990952,0.049425146874045804,0.10421370388939977,0.032693567840453464,0.006695818156003952,0.011664888166293802,0.2908155620098114,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,17.32838119007647,-1.8985915184020996,-1.8733670711517334,0.59603151679039,0.04659634886726306,0.0788984454702586,0.01699232531807951,0.005248211324214935,0.010680224529796781,0.2900856810156256,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,22.066579195903614,-1.4572886228561401,-1.4924654960632324,0.6558532863855362,0.03724377018886492,0.09274857537820935,0.0473366504314025,0.004467673599720001,0.007692787939463137,0.287921158131212,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",60,25.539541641017422,-1.595116376876831,-1.6436758041381836,0.6091942004859447,0.03832178291862517,0.11091155535541475,0.05425553731575455,0.006894323974847794,0.008985393605418833,0.28731463593430817,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",70,30.3377003648784,-1.9040608406066895,-1.89235520362854,0.5757062118500471,0.05363663542726388,0.06450040149502456,0.021258599748955144,0.003147505223751068,0.0073517015860289575,0.28922519087791443,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",80,33.89516538893804,-1.5388990640640259,-1.6808297634124756,0.6009221598505974,0.06642165014655718,0.09234411641955376,0.059294762862870255,0.004087571054697037,0.0084263024435034,0.28818007884547114,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",90,37.97749038413167,-1.9711315631866455,-2.0524003505706787,0.5756487399339676,0.06441246086468931,0.0823754274751991,0.054440909702476246,0.004098307341337204,0.010097731934521027,0.28818785795010626,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",100,42.131246546981856,-1.7148246765136719,-1.6784183979034424,0.5893787648528814,0.05881907576878933,0.07671518181450665,0.02928308960207721,0.004225678741931915,0.007194544908939998,0.2898117289878428,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",110,46.05968306493014,-1.7583365440368652,-1.8005545139312744,0.5841823481023312,0.06741913599732585,0.08449323615059257,0.04628885921560627,0.0045236460864543915,0.008999943698186805,0.2870887219905853,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",120,50.96887391200289,-2.1663122177124023,-2.1944785118103027,0.5280136708170176,0.039816360307379395,0.07234246947336942,0.05404664727619251,0.003873303532600403,0.007835097688611976,0.2765640988945961,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",130,55.099418504862115,-2.034902811050415,-2.1518585681915283,0.5577690210193396,0.054638534245144116,0.07462161243893206,0.03786414182840249,0.0028116852045059204,0.006062352723012684,0.2866584430448711,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",140,59.0499516769778,-1.9821691513061523,-2.01735782623291,0.5681939106434584,0.05054002878405625,0.08679471490904689,0.056625923307189636,0.002179577946662903,0.003408006992524444,0.2876136770937592,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",150,63.85653486405499,-2.1001858711242676,-2.213670015335083,0.5263309478759766,0.047886391517779514,0.06708617880940437,0.045749917063472124,0.0021850429475307465,0.005648687154119993,0.285013890825212,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.8772249429021031,0.41496366262435913,0.4154279828071594,0.9137866795063019,0.0274496169623729,0.4422777537256479,0.12040363573651419,0.06334507092833519,0.074110098543083,0.2868292569182813,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,4.943217786028981,-1.4104018211364746,-1.388014793395996,0.6591503694653511,0.039332931787753614,0.15566681325435638,0.09706901584375711,0.007203623652458191,0.010270068323949666,0.28769755689427257,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,9.143933461979032,-1.5176693201065063,-1.5210280418395996,0.664270531386137,0.08230444236845666,0.10818189615383744,0.05521942336633733,0.0036662817001342773,0.00572643099209282,0.2869420759379864,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,13.072703110985458,-1.2896287441253662,-1.3347060680389404,0.6983935423195362,0.042397474650716666,0.11063953302800655,0.04032161154395453,0.005548983812332153,0.010314556204415199,0.28433556389063597,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,17.156754358904436,-1.5344562530517578,-1.7372150421142578,0.5743398554623127,0.06604194490190009,0.1080983323045075,0.05832225533878664,0.0061292462050914764,0.013667623646445871,0.28685403196141124,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,21.614781849784777,-1.7493335008621216,-1.787076473236084,0.5903311893343925,0.053494694357044716,0.08484470192342997,0.04541012977748711,0.0022614337503910065,0.004579579125907942,0.28550729085691273,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",60,25.81121829082258,-1.9195443391799927,-1.8717657327651978,0.5655214972794056,0.043185485111481,0.09747175523079932,0.03439242133986252,0.00535694882273674,0.007427196728188029,0.28651465685106814,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",70,29.638618730939925,-1.637404441833496,-1.7588958740234375,0.6242360919713974,0.056141300553536715,0.07906307582743466,0.029811117170987306,0.003667019307613373,0.006858723064658255,0.2841894410084933,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",80,33.8386958159972,-1.672995924949646,-1.6598767042160034,0.6115069463849068,0.07106611233124668,0.08643523417413235,0.04823908684055906,0.0042410604655742645,0.008603603271806573,0.28694795817136765,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",90,38.49192253011279,-1.5621678829193115,-1.720019817352295,0.5929538868367672,0.057157064163157634,0.07329956046305597,0.0202436800949802,0.0037274137139320374,0.006401165769046989,0.28382759913802147,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.8674667798914015,0.07976065576076508,0.0988498330116272,0.8926657065749168,0.027604691349520438,0.3530222652480006,0.06210321259115536,0.05386226996779442,0.07207289009548178,0.28688292508013546,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,4.927870227955282,-1.1178990602493286,-1.1643548011779785,0.7374854609370232,0.05640829753924013,0.140124237164855,0.03631131351383957,0.007328081876039505,0.010615181498395238,0.2844459880143404,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,8.98126617190428,-1.5906476974487305,-1.669315218925476,0.6101662181317806,0.05515223345055887,0.09460300882346928,0.03347141098857647,0.004752758890390396,0.00919597524710344,0.28775809588842094,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,13.083775362931192,-1.685831069946289,-1.6988784074783325,0.5816667005419731,0.042329766226865656,0.10240105423144996,0.10230450761679366,0.004656404256820679,0.010724361923641502,0.28656038688495755,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,17.4355089829769,-1.7822504043579102,-1.825197458267212,0.6167890131473541,0.03251245389602639,0.09373901877552271,0.044343454084295596,0.0028206780552864075,0.003632949665860628,0.2874878831207752,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,21.81416138098575,-1.9109125137329102,-1.913405418395996,0.5824522934854031,0.04562085176881925,0.07049889909103513,0.03810645062931838,0.002338644117116928,0.005470227813636634,0.28524077008478343,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",60,25.892015038989484,-1.8169336318969727,-1.8507366180419922,0.575645312666893,0.030786518616917335,0.07274883636273444,0.029093224223802193,0.004419110715389252,0.010532477578597786,0.28643984883092344,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",70,29.486196144018322,-1.863667607307434,-1.9252070188522339,0.5572147220373154,0.042073508807921385,0.07188514969311655,0.03503562043730324,0.0020880363881587982,0.003575323555414074,0.2867993260733783,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",80,33.51866923482157,-1.915297269821167,-2.066838264465332,0.5654911585152149,0.05888419523886152,0.07707264344207942,0.03540978678075365,0.0023481473326683044,0.004089057966494116,0.2839093809016049,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",90,37.62331253406592,-1.9898602962493896,-2.1523849964141846,0.5542009118944407,0.045023440652240704,0.0972164049744606,0.06590570015027444,0.005814027041196823,0.008683733178311805,0.28542324900627136,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",100,42.66155590908602,-0.9481468796730042,-0.949081540107727,0.7192615084350109,0.06621127061781586,0.09999731392599642,0.033390541316039656,0.008002851158380508,0.010112487335693348,0.28484390093944967,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",110,46.071642505936325,-1.8809926509857178,-2.015104055404663,0.5554758831858635,0.03715561472546188,0.07091589039191604,0.019745248556889108,0.003807391971349716,0.009503951797349888,0.2861134430859238,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",120,51.23746221791953,-1.776033878326416,-1.8584157228469849,0.6442838665097952,0.06871942075360657,0.10365105327218771,0.06388444113663672,0.003424983471632004,0.005349495002969483,0.2866146641317755,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,0.8722593209240586,0.8209786415100098,0.79236900806427,0.9340204410254955,0.03173490781897519,0.5468475706875324,0.08237275598256877,0.0957515612244606,0.10276980143322598,0.2840257359202951,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,4.9661003248766065,-1.3674174547195435,-1.3811596632003784,0.6680869460105896,0.06606014044193846,0.1183354512322694,0.050947325253247834,0.005971740931272507,0.01047404180054524,0.2863385519012809,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,9.046514444984496,-1.2005062103271484,-1.2052743434906006,0.6803002543747425,0.028507000856391178,0.16619719099253416,0.10005226119782201,0.010976463556289673,0.015772632622572044,0.28512964001856744,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,12.958743776194751,-1.6020870208740234,-1.6420049667358398,0.6528041325509548,0.04081443742618342,0.10447016777470708,0.049709879977812635,0.004181988537311554,0.007539385829092369,0.2826964531559497,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,17.133486970094964,-1.8164043426513672,-1.7669415473937988,0.5991197340190411,0.02917155686221859,0.09041340043768287,0.04541192320740124,0.004675138741731644,0.008088324870344941,0.27981239603832364,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,21.30945805599913,-1.6562607288360596,-1.7306536436080933,0.6156931705772877,0.06334989675821856,0.09403987578116357,0.042748009873034724,0.006625164300203323,0.009753197171304514,0.2836957289837301,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",60,25.23098061280325,-1.6996029615402222,-1.7872402667999268,0.6322578862309456,0.046245948485675974,0.1061801475007087,0.03488104800883797,0.008030656725168228,0.011313123342967638,0.27961102803237736,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",70,29.11634512897581,-1.5486751794815063,-1.6632589101791382,0.6132759898900986,0.05735309548559253,0.10447175847366452,0.04395700168366572,0.0050962939858436584,0.008125613292762757,0.27702284906990826,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,1.4711928290780634,-0.20555345714092255,-0.22408488392829895,0.8575746826827526,0.04715024746024173,0.28949318546801805,0.0559780658335159,0.02619495987892151,0.03341536878300149,0.5098120120819658,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,8.631811923114583,-1.6256756782531738,-1.6218737363815308,0.6151698119938374,0.04379671785830551,0.09141171770170331,0.030006938330120286,0.0033311359584331512,0.00561217693178136,0.5006412789225578,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,15.741491532186046,-1.2260135412216187,-1.2093932628631592,0.6578641347587109,0.03736864444054881,0.10388120007701218,0.029464765284299855,0.007385782897472382,0.01679600730615315,0.5044856809545308,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,22.82898705196567,-1.7752704620361328,-1.7916224002838135,0.6204740833491087,0.05545050499289622,0.07666140445508063,0.04421879138854847,0.004219017922878265,0.011754252196415694,0.5076147438958287,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,30.08473795093596,-1.6599136590957642,-1.7415255308151245,0.6418449953198433,0.06762180029845642,0.10730291623622179,0.04576288720435058,0.00513119250535965,0.007198663848480066,0.5077382321469486,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,36.88812405010685,-1.9573689699172974,-2.0695788860321045,0.54330013692379,0.047967632096902475,0.06357000069692731,0.027629254666632082,0.0020309165120124817,0.004895087130714506,0.5105550980661064,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",60,44.25138659705408,-1.9766838550567627,-1.9979459047317505,0.5605131536722183,0.03820029424987444,0.0783833428286016,0.038252349662832794,0.0031590983271598816,0.005869292364010368,0.5090285229962319,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",70,48.22360982187092,-1.8931167125701904,-1.9287068843841553,0.5506900399923325,0.05292702007023967,0.09212823514826596,0.05667839003432398,0.0019952207803726196,0.0028684489641205907,0.48536251904442906,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",80,54.68526888987981,-1.889268159866333,-2.0340633392333984,0.5741297919303179,0.058555959019438705,0.07368993735872209,0.046627205648918264,0.0013282187283039093,0.0025657007222495932,0.48581767291761935,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",90,61.167953382013366,-2.0298285484313965,-2.081263542175293,0.5670728757977486,0.05121352195047625,0.07578477216884494,0.039361306029461855,0.005195070058107376,0.012063849538197164,0.4841815331019461,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",100,68.14285723003559,-2.003629446029663,-2.095710277557373,0.5504672303795815,0.051458002891542357,0.06892505055293441,0.022203406705257382,0.0025266744196414948,0.004377910936680573,0.48247269983403385,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",110,74.63320640195161,-2.1829962730407715,-2.2801718711853027,0.5508706551045179,0.04335407633112714,0.08171001682057977,0.042259204924529434,0.0019977614283561707,0.002904340054806747,0.49214196810498834,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",120,80.5127079160884,-1.8223206996917725,-2.0733091831207275,0.5792838782072067,0.04065304303213444,0.06824595073703676,0.03046554587190271,0.0016665160655975342,0.0036018250208484618,0.5075842910446227,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",130,88.25542083312757,-2.218289375305176,-2.2950708866119385,0.5422408133745193,0.021105249066021643,0.0793017263058573,0.040097731719662154,0.0024409815669059753,0.0035468894192955294,0.5004268691409379,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",140,94.65021585300565,-2.269622802734375,-2.4292619228363037,0.5261201430112123,0.034348482871135524,0.05410486925393343,0.02215841404737211,0.0009346790611743927,0.0018827782202891414,0.5192184539046139,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",150,100.44915300887078,-2.1282858848571777,-2.2512800693511963,0.5412197355180979,0.037992621527321475,0.07528328872285783,0.05935709056286542,0.0019294992089271545,0.0030189543799855538,0.4722115269396454,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",160,107.10184580390342,-2.2115676403045654,-2.327788829803467,0.5369907319545746,0.03579907938002586,0.05775135545991361,0.03095111282169846,0.0018148310482501984,0.0036213835053802855,0.47281942004337907,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",170,113.32048178906552,-2.176801919937134,-2.22729229927063,0.5635986048728228,0.06363292957029207,0.05574846314266324,0.026424643955251848,0.0006501004099845886,0.0018452349205230862,0.4681999471504241,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,1.5340177710168064,-0.01689387485384941,0.017413906753063202,0.8786894753575325,0.0662065056967256,0.3483186373487115,0.08686948412875935,0.028397992253303528,0.036996174616599875,0.4722832210827619,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,7.888482877984643,-1.330689549446106,-1.3129210472106934,0.6622300818562508,0.051003780957301625,0.10680101625621319,0.027457156769524513,0.007570318877696991,0.015025661026856962,0.4707199060358107,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,14.717791552189738,-1.3009166717529297,-1.355255365371704,0.6582567468285561,0.06319772146132002,0.10817655269056559,0.04905134469053168,0.004385523498058319,0.0069113340182708856,0.4708287010435015,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,21.23091315291822,-1.347059726715088,-1.3839598894119263,0.6852301172912121,0.06233470341042774,0.1176415083464235,0.03671700220151278,0.005788631737232208,0.0073444560786342416,0.4714546159375459,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,27.83522769412957,-1.9557300806045532,-2.0748982429504395,0.5740581545978785,0.05794918540395193,0.08019799110479653,0.03133193224441724,0.0019700191915035248,0.0027542327593681726,0.472110876115039,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,34.63356041000225,-1.226324200630188,-1.1822518110275269,0.6624743565917015,0.07956639795812252,0.08875490608625114,0.027372056527128017,0.004688888788223267,0.005288479995011005,0.47327462607063353,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",60,40.81655703205615,-1.8265764713287354,-2.0211775302886963,0.5709179155528545,0.04574466716630961,0.07796870917081833,0.02912057037189687,0.005260158330202103,0.012996751552104512,0.47371585783548653,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",70,47.690833740867674,-2.1197831630706787,-2.1673686504364014,0.5400347337126732,0.035864002962782485,0.058581862947903574,0.02865255756591497,0.0007400959730148315,0.001677399503644108,0.4693620109464973,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",80,54.066387319006026,-1.826760172843933,-1.9263733625411987,0.6105915680527687,0.07164151193699793,0.07462827023118734,0.025484264116833064,0.0019674599170684814,0.003904949796280193,0.47092867409810424,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",90,60.71682903007604,-2.003324031829834,-2.0476369857788086,0.5667697135359049,0.06624719558098606,0.07555350183974952,0.036446520420675275,0.001659926027059555,0.002819289676076592,0.4734410329256207,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",100,67.3937464109622,-2.1029911041259766,-2.0841784477233887,0.577752098441124,0.04524230119827588,0.05784743931144476,0.02059797712284796,0.0011528097093105316,0.002624552136169179,0.4713815769646317,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,1.5346985589712858,-0.04091606289148331,-0.10635125637054443,0.8495777957141399,0.05274533709057922,0.36868256237357855,0.07337462797630175,0.04000178724527359,0.0393329939821996,0.4715994948055595,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,7.9004293121397495,-1.3857694864273071,-1.4619051218032837,0.6406433023512363,0.05496871744992213,0.11419670842587948,0.048697994068911774,0.003603048622608185,0.004055813813359965,0.46886899694800377,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,14.670643158024177,-1.4572858810424805,-1.4559298753738403,0.6361686773598194,0.06795147450198856,0.10016681510023773,0.046181243169988506,0.004184223711490631,0.00590573332299138,0.4720463720150292,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,21.171380904968828,-1.9785505533218384,-1.9829459190368652,0.5672633275389671,0.06432234600502136,0.07894448679871857,0.039851172695527924,0.0020296238362789154,0.0035820612508290824,0.4708700948394835,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,27.85473139001988,-1.8752174377441406,-1.894749641418457,0.6132137440145016,0.05062287812442114,0.10457042488269508,0.05814471047377076,0.003293994814157486,0.005846442055525786,0.4716891630087048,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,34.53101989487186,-1.980480432510376,-1.9717085361480713,0.5685391146689653,0.051874777911477155,0.10070122010074556,0.050148576337174525,0.004441812634468079,0.005115282417621031,0.4759675550740212,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",60,41.090900572016835,-1.9814190864562988,-2.046090602874756,0.5780270807445049,0.03677589093508765,0.10729889152571559,0.036028936902954035,0.0039293281733989716,0.00476624772333795,0.47596158902160823,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",70,47.67433117493056,-1.6802319288253784,-1.7584608793258667,0.6165138110518456,0.07276220602221663,0.0850969438906759,0.048156395971434285,0.0017391741275787354,0.00339840726669751,0.474601143039763,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",80,54.45478442683816,-2.0549087524414062,-2.1886637210845947,0.5536166504025459,0.037294626920504836,0.06502724532037973,0.02434593977625191,0.0009447336196899414,0.0017286545897137553,0.4664151961915195,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",90,61.409829291980714,-2.059967279434204,-2.1230852603912354,0.5419542752206326,0.034595402350727715,0.06342308386228979,0.02479319992321635,0.0011574625968933105,0.002241788986818458,0.46956892195157707,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",100,68.40854086913168,-1.750996708869934,-1.8790521621704102,0.5942079108208418,0.06030539434249842,0.08909157128073275,0.05015979088802412,0.002919767051935196,0.004519700189285266,0.4696383860427886,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",110,75.11671487498097,-1.620972752571106,-1.733769178390503,0.6246047765016556,0.04202945881363175,0.10011581145226955,0.046785899687673646,0.005582630634307861,0.007715463862006115,0.4701379439793527,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",120,80.96002128813416,-2.193005323410034,-2.2426767349243164,0.532679570838809,0.05022508266069097,0.06682127236854285,0.041360507890137864,0.0010675713419914246,0.002331304436082404,0.47051358688622713,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",130,88.09729454503395,-1.907700777053833,-2.071913719177246,0.5574935134500265,0.04502056481204045,0.0715413331054151,0.04061213408571468,0.0014142803847789764,0.003152757984680688,0.4712152371648699,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",140,94.92435930902138,-1.924607753753662,-2.0800979137420654,0.5713163446635008,0.05341887945791098,0.08622762304730713,0.04291152950080523,0.002955641597509384,0.0051867914878583245,0.467375730862841,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",150,101.98429655400105,-1.9849947690963745,-2.1443285942077637,0.555536326020956,0.05379911293786805,0.07195684034377337,0.02920314168894673,0.0018424466252326965,0.003968742334303791,0.46993508585728705,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,1.361574579961598,0.8227875828742981,0.8435159921646118,0.9330153837800026,0.028799713815955617,0.5102399419993162,0.09306616594580228,0.06993366219103336,0.09098143753165973,0.4691157890483737,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,7.986795379081741,-1.3065834045410156,-1.4401676654815674,0.6460586525499821,0.03953353044611983,0.11729038320481777,0.02608344479219726,0.006819192320108414,0.01096142712297169,0.47457773704081774,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,14.546819432871416,-1.615862488746643,-1.677600383758545,0.6112262234091759,0.05078713537427691,0.09495000797323883,0.03961140239320758,0.0030795373022556305,0.0039787872785112645,0.47258572303690016,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,21.56828508595936,-1.7126375436782837,-1.7525899410247803,0.5792170129716396,0.04991546854081459,0.1048561348579824,0.03846322982215041,0.004644215106964111,0.005722184215242339,0.4674486629664898,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,28.071024026023224,-1.8031401634216309,-1.823050856590271,0.6352748293429613,0.06575155057541954,0.09015051135793328,0.030104686383822608,0.002598673105239868,0.003009052921354187,0.4690863511059433,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,34.87792125204578,-1.6487809419631958,-1.6751391887664795,0.605594027787447,0.06352901791498859,0.10167150595225394,0.05177147157858826,0.0033480413258075714,0.0038144924707464024,0.47520856792107224,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",60,41.37856685114093,-1.8749122619628906,-1.9708222150802612,0.5872819647192955,0.036994143067588275,0.07803043210878968,0.018856129235386352,0.003934301435947418,0.006611926076870483,0.4710420030169189,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",70,48.28536395006813,-1.6531203985214233,-1.6576933860778809,0.6145289652049541,0.03887088101987778,0.08149958052672446,0.024468059193755238,0.0025192201137542725,0.0035495972294591646,0.46605511801317334,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",80,55.26388486195356,-1.8235139846801758,-1.9224865436553955,0.579801645129919,0.05995019789456504,0.07737783086486161,0.035726023351948036,0.0025886371731758118,0.0068182985389011914,0.4656342479865998,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",90,61.506740730954334,-1.6173368692398071,-1.8073879480361938,0.606710147112608,0.0494462787054867,0.10893880086950958,0.050826411949719465,0.004703804850578308,0.006912912985853763,0.4713685689494014,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,1.3650894239544868,0.7263491153717041,0.6451373100280762,0.9181172847747803,0.02989047779890736,0.4908230025321245,0.1288845294129925,0.059723109006881714,0.07407762778349271,0.47116982890293,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,7.969067886006087,-1.3862980604171753,-1.4257574081420898,0.643250934779644,0.05329343298339316,0.1035276222974062,0.029633847991552116,0.005456931889057159,0.007906155628864734,0.46922068390995264,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,14.41590741998516,-1.668276071548462,-1.728111743927002,0.58700205758214,0.04066761549871053,0.09148665005341172,0.02834511096561414,0.003906574100255966,0.007421758479545254,0.47140505304560065,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,21.187729655066505,-1.602404236793518,-1.6380102634429932,0.6368081010878086,0.07001197244217475,0.09819029946811497,0.026387715643576053,0.006750177592039108,0.01402560383948807,0.4702337409835309,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,27.63969793007709,-1.6922779083251953,-1.7919847965240479,0.5791295766830444,0.04164848709237914,0.10311074065975845,0.053801689186085924,0.004837267100811005,0.011778881397314724,0.4695310278329998,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,34.27271786890924,-1.90518057346344,-2.0082767009735107,0.5846827533096075,0.06585450217320153,0.08052667719312012,0.03499022169795914,0.0016228482127189636,0.0027345440184837113,0.47246790188364685,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",60,40.75826138490811,-1.8404459953308105,-2.024764060974121,0.5533719174563885,0.036811422960332525,0.08234998979605734,0.04060655247345484,0.0060660094022750854,0.017628869404438526,0.47342036198824644,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",70,47.146293066907674,-1.7892248630523682,-1.903498649597168,0.5847681742161512,0.06874464471635858,0.09555341931991279,0.04950167334549281,0.002675119787454605,0.003981754967384537,0.46651239483617246,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",80,53.693071625195444,-1.9117352962493896,-1.92241370677948,0.5713832806795835,0.07416833464180426,0.07429340109229088,0.04956703778593915,0.001387912780046463,0.0026656661585348932,0.46876198682002723,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",90,60.4807928269729,-1.822100281715393,-1.981269121170044,0.596094436943531,0.03602230230640671,0.07482680073007941,0.029071375656762936,0.0021051056683063507,0.0036836114074774184,0.47204531403258443,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",100,67.54450104781426,-1.8854807615280151,-1.9762178659439087,0.5999115221202374,0.050618180574947234,0.0914103607647121,0.047400003985874566,0.002101246267557144,0.004260647349935302,0.47431059600785375,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",110,73.73423130088486,-2.063434600830078,-2.196776866912842,0.5401844959706068,0.03944028140384958,0.06629956350661814,0.025490632390489185,0.0008695311844348907,0.0017724445486536596,0.47616792493499815,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",120,80.65932662016712,-2.1642065048217773,-2.2221364974975586,0.5520312767475843,0.03207104741341632,0.06130621023476124,0.02410137500848374,0.0015104226768016815,0.0031078306400262735,0.47640097117982805,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",130,86.91740366490558,-1.3863762617111206,-1.5437135696411133,0.6589092239737511,0.04320573579902749,0.13811710150912404,0.06550400354131021,0.008812550455331802,0.012730755652248703,0.46873797313310206,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",140,93.7105809841305,-2.164304494857788,-2.237412214279175,0.5668959096074104,0.04170931000916713,0.07514263270422816,0.05959173659571897,0.0012484118342399597,0.0020960786940117737,0.469131130957976,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",150,99.94700427399948,-2.155299663543701,-2.2801613807678223,0.5220915880054235,0.04286498152961294,0.06242035503964871,0.031176262674214663,0.0012987330555915833,0.002293739705063881,0.4708792599849403,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",160,106.68872878886759,-2.1631338596343994,-2.176839828491211,0.5783035606145859,0.05385258934069111,0.07891522953286767,0.032907878695932404,0.0019128844141960144,0.0026178171150854574,0.4700001939199865,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",170,113.19743983098306,-1.9642460346221924,-2.024104118347168,0.5711007658392191,0.04235714753864051,0.07897303067147732,0.054802823669479826,0.0020793527364730835,0.0032646964776886374,0.46538117807358503,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,1.4955953371245414,0.7058202028274536,0.6400260925292969,0.9132996909320354,0.0400526471555158,0.5021077338606119,0.09710083392529512,0.06177648529410362,0.07889482845321961,0.4651173809543252,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,7.815282674971968,-1.3816548585891724,-1.393560528755188,0.6762727349996567,0.03446073210017918,0.12606254499405622,0.050983958504930434,0.004218257963657379,0.005750383863148098,0.4719272570218891,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,14.543064882047474,-1.444570541381836,-1.6028896570205688,0.5987247340381145,0.09747538698251851,0.10364122642204165,0.034259514139023645,0.004965197294950485,0.010380421420181593,0.46724877902306616,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,20.89374838396907,-1.671383261680603,-1.7910175323486328,0.5870317183434963,0.044038618174350404,0.09793032193556428,0.05364880403619709,0.0036421306431293488,0.005186369822923724,0.47049323888495564,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,27.460256403079256,-1.442814826965332,-1.4124267101287842,0.6888401955366135,0.05534088368013836,0.10962893022224307,0.05154120800595125,0.004754293709993362,0.006317863672988205,0.46789444680325687,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,33.90997706982307,-1.8794854879379272,-1.8957045078277588,0.5648804418742657,0.040472030005386656,0.08173198881559074,0.0432649314598982,0.0015721134841442108,0.0023104290921933564,0.46464012120850384,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",60,40.37841507093981,-1.9629935026168823,-1.8660376071929932,0.58891361951828,0.08983124008980388,0.07782840414438397,0.0375861852130095,0.00391850620508194,0.0066827924305880824,0.46723004803061485,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",70,47.01254415907897,-2.078191041946411,-2.1689605712890625,0.5530937276780605,0.0448329972332583,0.07272239308804274,0.061370940833716366,0.003381546586751938,0.009095047844376774,0.46265558898448944,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",80,53.45201459294185,-1.8402358293533325,-1.954514741897583,0.6330282762646675,0.03557006603912505,0.09931883122771978,0.06434461138279968,0.0033372119069099426,0.006116653910230115,0.4655732859391719,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",90,60.326265519019216,-2.035830020904541,-2.0332789421081543,0.5584951378405094,0.06874790058316363,0.08907950238790363,0.05242937896271088,0.0018705986440181732,0.0025714886931379774,0.4639729701448232,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",100,66.98823763709515,-2.062835693359375,-2.196021795272827,0.5587417259812355,0.04288222100535358,0.07893079018685967,0.03800584373248798,0.003202766180038452,0.0045404578570118976,0.46685698395594954,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,1.3501836508512497,0.10497453808784485,0.18735022842884064,0.8885513506829739,0.04531004463449126,0.4157058959826827,0.10040612152849963,0.046856749802827835,0.05449492908968463,0.4667721651494503,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,7.9426052388735116,-1.2162588834762573,-1.2657233476638794,0.693326972424984,0.06224810289637436,0.10979175893589854,0.027165508022715598,0.0033697672188282013,0.005584783290569899,0.46705565601587296,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,14.407277195015922,-1.551723599433899,-1.5946205854415894,0.6259787380695343,0.05128492024529554,0.1042791847139597,0.030674966503169122,0.004368312656879425,0.006659499993024302,0.4685280139092356,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,20.919429098954424,-1.3659857511520386,-1.4417779445648193,0.6751691363751888,0.06126857256246811,0.0987812327221036,0.021949425317031073,0.0054845139384269714,0.010916291003601567,0.46661939402110875,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,27.573358092922717,-1.935317039489746,-1.9987889528274536,0.5901324637234211,0.058412026886001764,0.07422859780490398,0.040450060941580566,0.0009718425571918488,0.0016413656986686653,0.4660558260511607,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,33.93751615192741,-1.7078906297683716,-1.6938393115997314,0.6171281859278679,0.054438001077178874,0.09851281996816397,0.029111617728349495,0.004849456250667572,0.006411351406964506,0.46479957294650376,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",60,40.75852753804065,-1.7438265085220337,-1.7690101861953735,0.627106711268425,0.0774715896268057,0.1380836833268404,0.07558173039008528,0.005432344973087311,0.004851463123549792,0.46585801406763494,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",70,47.73063542181626,-2.2491276264190674,-2.3460850715637207,0.5313903614878654,0.03532042740607596,0.06471594923641533,0.031166337756041156,0.0014944374561309814,0.0029369325023477118,0.4684177387971431,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",80,54.01124198595062,-2.076734781265259,-2.0760140419006348,0.5469320639967918,0.04624440014962371,0.07730179443024099,0.038196238836251326,0.001539323478937149,0.002211095329695043,0.47055281000211835,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",90,60.24545633397065,-2.080322027206421,-2.2620809078216553,0.5343590900301933,0.03368331828403505,0.0666383970528841,0.03119593953442735,0.0022937096655368805,0.00595795862111096,0.46933152503333986,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",100,67.84564704913646,-1.829890251159668,-1.8142763376235962,0.6029975675046444,0.05216966247522245,0.09577223309315741,0.046406124375376355,0.002956785261631012,0.004298404852272323,0.4683580871205777,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,1.3546628311742097,0.02684447169303894,0.12852376699447632,0.8837246596813202,0.05682708922162884,0.35920293629169464,0.09995204373809674,0.03433435037732124,0.044525706750652295,0.46885047596879303,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,7.925779019948095,-1.411568284034729,-1.5157486200332642,0.6281681284308434,0.05288159571959347,0.08740470372140408,0.022859278606631155,0.0031718313694000244,0.005280292618805664,0.47204393008723855,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,14.539054193999618,-1.3821431398391724,-1.405102252960205,0.649906150996685,0.06958882350900655,0.10440859105437994,0.035312524443061086,0.00450514629483223,0.0057764979291443554,0.46921335719525814,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,21.256637517130002,-1.8980152606964111,-1.860548973083496,0.5787965916097164,0.035528660071445686,0.08606567629612982,0.03234611807906183,0.001987732946872711,0.002250180055288239,0.4695428549312055,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,27.779081154149026,-1.7851898670196533,-1.9024837017059326,0.557580279186368,0.053231110750545936,0.09213676629588008,0.06384983199294421,0.0016769766807556152,0.0017705018384462673,0.47107102698646486,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,34.534824437927455,-1.7791392803192139,-1.81821608543396,0.6085488833487034,0.04684998430306902,0.07279330655001104,0.03343345340822024,0.0023802779614925385,0.0052134408838210985,0.46895494498312473,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",60,40.75188030209392,-1.7153949737548828,-1.7708749771118164,0.6341485027223825,0.07559274058551765,0.09509552456438541,0.039188860791213,0.0018770880997180939,0.0032250421016893826,0.4652311059180647,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,1.345653659896925,0.0912826731801033,-0.015815220773220062,0.8688978180289268,0.03814416968883787,0.3627724461257458,0.0922472806697645,0.03841971606016159,0.04967548889146823,0.46415938390418887,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,7.8163772479165345,-1.4141793251037598,-1.5402307510375977,0.6534359492361546,0.05255312516784214,0.11853126669302583,0.043258561815430065,0.005128063261508942,0.009558193499960529,0.4638387719169259,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,14.035856943111867,-1.729914903640747,-1.754710078239441,0.6260387469083071,0.05788132940990944,0.07447345368564129,0.02304849554547446,0.0008604228496551514,0.001553639148785442,0.4593185121193528,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,20.613531808834523,-1.517639398574829,-1.5884348154067993,0.6173374988138676,0.04821969531646276,0.0983132841065526,0.03587738987571418,0.004550911486148834,0.006982313762177849,0.4583979700691998,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,26.767342943931,-1.9240357875823975,-1.9330273866653442,0.5724796503782272,0.04427435184092424,0.07646344904787838,0.03408536298850119,0.0025981254875659943,0.005003440381392229,0.456244939006865,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,32.91571863088757,-1.863865613937378,-1.974661111831665,0.5864544026553631,0.05821416494259908,0.09121531178243458,0.03469528501206569,0.002852741628885269,0.004456557732678352,0.4546127859503031,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",60,39.20164448302239,-1.812103271484375,-1.796545147895813,0.5982629433274269,0.0523346541273948,0.08746093045920134,0.04860985860170418,0.002802222967147827,0.003593315362549359,0.45508486893959343,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",70,45.50326957600191,-1.7229901552200317,-1.9409589767456055,0.5839201789349318,0.06937049993789346,0.10215585585683584,0.0633679917658035,0.0036868564784526825,0.005250574427003355,0.4567849689628929,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",0,1.316722186980769,0.0040757134556770325,0.00337606412358582,0.8819569610059261,0.02871335388604573,0.3214746592566371,0.06793224779562375,0.03189019113779068,0.044084846662207494,0.4537848769687116,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",10,7.700170998927206,-1.436201572418213,-1.3859301805496216,0.6674487963318825,0.05063371326418588,0.12473570043221116,0.053495483856252225,0.004156332463026047,0.0065019560092415395,0.4552551649976522,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",20,13.895194977056235,-1.7058067321777344,-1.676435112953186,0.6014388985931873,0.03446139455318171,0.10389144718647003,0.029237594844858,0.006338436156511307,0.010531164007636117,0.4538249501492828,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",30,20.424490490928292,-1.5960474014282227,-1.5930677652359009,0.6400789432227612,0.07635913401128719,0.08788546500727534,0.03836309652497935,0.0023027323186397552,0.0038229314986442642,0.45774263399653137,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",40,26.47289004107006,-1.4886776208877563,-1.5592446327209473,0.6034739017486572,0.07060759734621659,0.09322416898794472,0.049746999010815945,0.002127360552549362,0.004645057422729434,0.4534412438515574,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",50,32.92612943123095,-1.7914561033248901,-1.9265960454940796,0.6110116820782423,0.07307923728661529,0.07972489320673048,0.02865228207658222,0.001637183129787445,0.003649711852174048,0.45696218591183424,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",60,39.050598569912836,-1.8522264957427979,-2.0116899013519287,0.5645611695945263,0.05968671928671225,0.06661480595357716,0.020561862334485863,0.002253524959087372,0.005872571642946641,0.45035015302710235,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",70,45.32790080900304,-1.3061387538909912,-1.4854300022125244,0.6623559072613716,0.0458664418121174,0.10819770325906575,0.047616850480290004,0.00820639356970787,0.023774208409101407,0.4517919709905982,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",80,51.49448839109391,-2.0667483806610107,-2.155132532119751,0.5525220632553101,0.04020513352594381,0.07461093598976731,0.04141957152580114,0.0017265193164348602,0.0026814777968846206,0.4520881378557533,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",90,57.89654930494726,-1.687645435333252,-1.7745529413223267,0.6054008193314075,0.05954170484439775,0.08248746953904629,0.034870200477053255,0.0020743906497955322,0.003890007725907834,0.45407876186072826,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",100,64.02480673603714,-1.9625773429870605,-2.091465473175049,0.5489775780588388,0.03146663589722904,0.07371652801521122,0.030380311852527427,0.002100009471178055,0.0037081346931290447,0.4552132918033749,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","npe_sbi[flow=nsf,transforms=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural posterior estimation (NPE).\n\n    The solver trains a parametric conditional distribution :math:`q_\\phi(\\theta | x)`\n    to approximate the posterior distribution :math:`p(\\theta | x)` of parameters given\n    observations.\n\n    Implementated with the :mod:`sbi` package.\n    ",110,70.53509746212512,-1.9938215017318726,-2.0513007640838623,0.5724205356091261,0.052783742608899224,0.0889478197786957,0.051541912998508035,0.00224250927567482,0.002991545776411749,0.45185191091150045,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.0013491869904100895,2.483445644378662,2.483398199081421,0.9822988249361515,0.004836688066753179,1.3106432408094406,0.3020993690427473,0.3025434445589781,0.18098414165547233,27.038786774035543,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.6808654931373894,-0.8100945949554443,-0.6870539784431458,0.7796863950788975,0.05475959792919817,0.1580262891948223,0.05232314640116364,0.012007128447294235,0.011397647279278212,28.232232151087373,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,1.1376603278331459,-0.8495593667030334,-0.8375630378723145,0.7282721102237701,0.06774696751575644,0.368414921220392,0.2077154042595484,0.06458386406302452,0.05630911203756527,19.525462127057835,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,1.5993050104007125,-1.2981566190719604,-1.3043521642684937,0.6677865348756313,0.07737668348613923,0.3094066265039146,0.1735537324688516,0.05716486647725105,0.07305085826898668,21.136905639898032,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,2.0619466793723404,-1.6412345170974731,-1.5272486209869385,0.6107464656233788,0.08248249773128055,0.14267418673262,0.09048722389764716,0.009011078625917435,0.011411620591055369,18.748287989990786,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,2.5277749050874263,-1.3631592988967896,-1.3534334897994995,0.6353652663528919,0.08385236154808048,0.1987478486262262,0.09029426697720973,0.025151900947093964,0.023754445430721076,17.453599851112813,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,2.9883661011699587,-1.8919726610183716,-1.8223350048065186,0.6644456349313259,0.08359796586931997,0.17105161491781473,0.07021037720411329,0.015846557915210724,0.012878839116547315,16.427480230107903,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",70,3.47157333814539,-1.8669695854187012,-1.832180380821228,0.6884404309093952,0.06706067228719538,0.1662779706530273,0.09107312970577813,0.011986762285232544,0.01898476157050855,21.1351983291097,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",80,3.9323807167820632,-1.6849535703659058,-1.6370699405670166,0.6955550499260426,0.06863601635671082,0.2232366823591292,0.12957845686034947,0.042312897741794586,0.05170138955216907,16.264013400068507,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",90,4.411619510501623,-1.94269859790802,-1.8646665811538696,0.6455635335296392,0.09099833882979026,0.14786951313726604,0.09504133177153268,0.013329550623893738,0.014938453731616575,17.404032546794042,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",100,4.8682958777062595,-2.0875680446624756,-2.055013418197632,0.6054366715252399,0.059564899482173195,0.17721439199522138,0.1273777651311461,0.01907980442047119,0.031161181523350002,16.884538940154016,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",110,5.327047091675922,-2.1651039123535156,-2.100358486175537,0.6861243173480034,0.0943959090340526,0.19114635000005364,0.1424696799677652,0.022363994270563126,0.02585268902318217,19.016043210867792,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",120,5.79427506076172,-2.3992996215820312,-2.386542320251465,0.6380258202552795,0.05608283459215689,0.22919384064152837,0.09644914339222196,0.023703020066022873,0.022023680760517358,23.061994907911867,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",130,6.257260059705004,-2.367802381515503,-2.2867283821105957,0.7025424167513847,0.03129286723325636,0.3684265813790262,0.17866623463839504,0.09065423160791397,0.06928787314621114,18.441108799073845,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",140,6.718550376826897,-0.9032418727874756,-0.9761386513710022,0.7014021389186382,0.08797401355011715,0.23270914377644658,0.18998818147050064,0.033589065074920654,0.05844523524938299,16.2663926940877,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",150,7.173583106836304,-2.4595024585723877,-2.425889492034912,0.6148541979491711,0.07837366815420566,0.11685791681520641,0.05969147139642552,0.004621569067239761,0.006750789622702316,16.96658943593502,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",160,7.627608907641843,-2.571380138397217,-2.5369081497192383,0.6244676969945431,0.05889542089190074,0.1793903422076255,0.1030372440889388,0.01453930139541626,0.015761598004289175,17.465368133038282,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",170,8.091522665228695,-2.4572560787200928,-2.4286961555480957,0.6392493695020676,0.056069447891276755,0.23713848018087447,0.17187942078765442,0.032439686357975006,0.04597790765084074,15.527635340113193,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",180,8.558443268062547,-1.8675062656402588,-1.7958539724349976,0.5950177423655987,0.05561868290897295,0.13703129044733942,0.08882829009865045,0.010400176048278809,0.013944396538275781,15.825639018090442,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",190,9.021975479088724,-2.195350170135498,-2.133784055709839,0.588852871209383,0.05627962673783093,0.11422485462389886,0.052530209298292524,0.005570665001869202,0.005747675498379403,15.884231112198904,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.0014998889528214931,2.3609561920166016,2.362332820892334,0.9819329641759396,0.0046624167489122854,1.3019756712019444,0.30457561798228927,0.29903144389390945,0.18002323912078289,16.366670174058527,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.47073343582451344,-1.4714516401290894,-1.3232239484786987,0.7595516964793205,0.06623594377098345,0.2803390873596072,0.16146833757070775,0.030947253108024597,0.0336678068910458,15.03653229190968,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,0.937180849025026,-1.0919166803359985,-1.094826340675354,0.7024538591504097,0.06412295703636144,0.14786571729928255,0.06252007013322604,0.011263526976108551,0.010165759519883496,15.188074688892812,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,1.4113967914599925,-2.006136655807495,-1.9896986484527588,0.6244420483708382,0.08985007658098197,0.11621468793600798,0.05216262736803262,0.007866684347391129,0.010652391975401824,15.291999218985438,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,1.8791196367237717,-1.7095344066619873,-1.6469472646713257,0.6863341517746449,0.06628678720924999,0.22489406121894717,0.14438997697303838,0.03153345733880997,0.03457311600369149,15.32863749214448,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,2.3397588646039367,-1.9390276670455933,-1.8557806015014648,0.6113883517682552,0.0616275616484657,0.13772933837026358,0.08668556484497734,0.008559785783290863,0.011280388082338666,14.563274825923145,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,2.806543816346675,-1.8437306880950928,-1.7739083766937256,0.6776780784130096,0.04890351689435678,0.3214509633835405,0.18369180846810337,0.0628490261733532,0.0615949953364952,14.395196616882458,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.0014911028556525707,2.5926222801208496,2.5921530723571777,0.982575599104166,0.0053673634594866414,1.3086037151515484,0.3053051541826606,0.30115585401654243,0.1825247456444541,13.777975488919765,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.45207716058939695,-0.7147655487060547,-0.6277362108230591,0.7577205672860146,0.05578934992358588,0.25650757597759366,0.13953623535437384,0.027641650289297104,0.027331725576792875,13.898927060887218,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,0.888097474584356,-1.6298809051513672,-1.5002646446228027,0.6854156441986561,0.08129134435486728,0.17551036877557635,0.09533074573040208,0.01684526354074478,0.018814567681812198,12.299365637125447,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,1.3195015923120081,-1.5098938941955566,-1.4538967609405518,0.6887113936245441,0.07766946597343055,0.21966505469754338,0.10531385910190948,0.022643212229013443,0.02086690787419146,12.165639324113727,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,1.7532709995284677,-2.117617607116699,-2.0091843605041504,0.6272266544401646,0.07928298141127545,0.1460647266358137,0.06604842313865565,0.010343324393033981,0.013268039224165577,12.320119122974575,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,2.185954856220633,-2.445230007171631,-2.334083080291748,0.6725775897502899,0.06372416957706045,0.17557756695896387,0.07519383892833777,0.01741066575050354,0.021266617513898495,12.30537859397009,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,2.620148597052321,-1.8739193677902222,-1.8174700736999512,0.636535719037056,0.06668502999489316,0.1710403780452907,0.10780691056215241,0.013088557869195938,0.014248756271406782,12.32108864095062,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",70,3.055890184128657,-2.121572494506836,-2.0306396484375,0.6853888854384422,0.05235127064012736,0.1966711999848485,0.09035413963265271,0.018688317388296127,0.018164383317552497,12.38819688395597,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",80,3.490581192774698,-2.348052978515625,-2.2696239948272705,0.6972373835742474,0.06804444411530593,0.21330290520563722,0.10255346190271294,0.03176698833703995,0.03542399497399621,12.373960227938369,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.009357989998534322,2.624102830886841,2.619978189468384,0.9824522286653519,0.004507008565086391,1.2947304472327232,0.3041105155233122,0.2987942174077034,0.18022280868470067,12.530188051052392,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.4428844565991312,-0.6399908065795898,-0.6174682378768921,0.734831690788269,0.0655118632599739,0.18514312151819468,0.10020090220163366,0.014225635677576065,0.01781987764638199,12.450043838936836,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,0.876502797473222,-1.877867579460144,-1.766841173171997,0.6908163093030453,0.0758723513873106,0.2589912675321102,0.14433892479898552,0.04637877643108368,0.05729538252969692,12.58321237610653,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,1.3115869562607259,-0.9827597141265869,-0.9200502634048462,0.6446711216121912,0.08258572242966561,0.1565269734710455,0.1042406260289524,0.011843759566545486,0.020919667975030122,12.58345761988312,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,1.7445692785549909,-1.3059394359588623,-1.2054301500320435,0.7224762998521328,0.0589899853915797,0.1656063215341419,0.10104542093973869,0.02151203155517578,0.02468835500079475,12.206662077922374,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,2.1842917911708355,-2.253356456756592,-2.1814417839050293,0.666010644286871,0.05333967627332541,0.13862515660002828,0.08020313033141775,0.016726970672607422,0.028276560929820123,12.32914871093817,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,2.6186964649241418,-1.0660786628723145,-0.994910717010498,0.7011958882212639,0.07718738265590294,0.20502748992294073,0.13068336313215395,0.018944870680570602,0.023043287110504004,12.521999035961926,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",70,3.0522921113297343,-2.3000199794769287,-2.3270926475524902,0.6512399315834045,0.059934457530429704,0.19017874845303595,0.11003088216133533,0.022950883954763412,0.028578406317891564,12.394448360195383,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",80,3.4852846669964492,-1.8167290687561035,-1.742484211921692,0.6447898652404547,0.07557817739048704,0.10890423133969307,0.05421482388627876,0.006627816706895828,0.00853751029514547,12.376504604006186,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",90,3.9155620341189206,-2.1528849601745605,-2.1161887645721436,0.6114483587443829,0.07406324620722415,0.18540253629907966,0.09390036094585481,0.0184846892952919,0.022896790684588997,12.453358991071582,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",100,4.351301385322586,-1.730758547782898,-1.6426531076431274,0.6579181030392647,0.0579195024242563,0.1752769141457975,0.07832656735437292,0.01772206649184227,0.020225965985027655,12.350265812128782,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.0016409549862146378,2.447883129119873,2.4454703330993652,0.9824215210974216,0.00495027972149651,1.3023792877793312,0.29368742285998267,0.3014430105686188,0.18403876358946103,12.324857035884634,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.43681395100429654,-0.7033371329307556,-0.6893056631088257,0.7820125669240952,0.05263046143021606,0.21977910911664367,0.0773438280630501,0.0214400514960289,0.018528760130863686,12.117232602089643,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,0.8702847340609878,-1.7689019441604614,-1.6798646450042725,0.7164974100887775,0.0744810633774153,0.20520571107044816,0.08832414939650197,0.01913931593298912,0.01924579115573715,12.023351970128715,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,1.3045660210773349,-1.0999079942703247,-1.0408109426498413,0.6713155247271061,0.08307989915561192,0.19134350586682558,0.08435477270625678,0.020257629454135895,0.018084146303913043,12.179708101088181,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,1.741110764676705,-1.2386665344238281,-1.1566133499145508,0.63921819627285,0.08377495055017646,0.2879858617670834,0.15659097139119482,0.03905988484621048,0.039050126782161444,12.097769628977403,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,2.1794364219531417,-1.7632564306259155,-1.7036375999450684,0.6594838015735149,0.08226342088734098,0.1914272252470255,0.09088104785877946,0.017870161682367325,0.014985185431717589,12.146661996142939,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.0016798470169305801,2.4531049728393555,2.4535319805145264,0.9812307767570019,0.004434272652623455,1.3011301010847092,0.29488940134657765,0.299705995246768,0.1791496711127423,12.583999519934878,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.43516143155284226,-0.7567418813705444,-0.7656286954879761,0.7698666453361511,0.05953831170145837,0.1747190747410059,0.05171349504643902,0.013348907232284546,0.014066451529139353,12.128138177096844,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,0.8639876022934914,-1.5187311172485352,-1.4631527662277222,0.7077627591788769,0.07487213087624096,0.14324847934767604,0.05554450057949355,0.009046979248523712,0.008686345796526454,12.130876873852685,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,1.2976259186398238,-1.7812854051589966,-1.711072325706482,0.669029762968421,0.07821553080522507,0.14866787591017783,0.08693933509864922,0.0155288465321064,0.023397535175475364,12.276680169859901,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,1.730690435739234,-1.9585899114608765,-1.9338161945343018,0.6475535240024328,0.07846286685947794,0.24981754692271352,0.16407703215441885,0.027526002377271652,0.02930525048533556,12.217640940099955,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,2.1637525060214102,-1.7248921394348145,-1.7431672811508179,0.6624201610684395,0.0541100169549695,0.21911569824442267,0.12005555585029905,0.03262075409293175,0.02718828378561437,12.399278632132336,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,2.5958439162932336,-2.3444252014160156,-2.284282684326172,0.6973865516483784,0.03665968788302295,0.19983733724802732,0.1716712075424353,0.042488016188144684,0.07511439249051527,12.119474272010848,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",70,3.0275246074888855,-2.3496346473693848,-2.3625011444091797,0.6363800801336765,0.062279509734908894,0.16050045751035213,0.06866891181312719,0.01778816059231758,0.017792991887047162,12.063387346919626,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",80,3.4582409185823053,-2.229921817779541,-2.115692615509033,0.6654857136309147,0.047370484992011026,0.37082677567377687,0.1706157804468297,0.08098053187131882,0.05658518805550092,12.253575755050406,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",90,3.8909183198120445,-3.225264072418213,-3.1737287044525146,0.6622792072594166,0.0823182071688998,0.14078651182353497,0.08484337424641498,0.008423786610364914,0.010598901746918521,12.14936261693947,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",100,4.324274938786402,-1.6691031455993652,-1.716876745223999,0.6280150040984154,0.06839837809241149,0.15549056325107813,0.07951693532122492,0.010114513337612152,0.009920791956825388,12.31985190603882,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",110,4.758581386646256,-2.6567559242248535,-2.61965274810791,0.6248514391481876,0.061417873246025066,0.1530605386942625,0.0736284458445623,0.009398162364959717,0.009023750840227624,12.482539716176689,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",120,5.191069396212697,-2.6970014572143555,-2.600600481033325,0.6780986487865448,0.05746372155755179,0.38601266872137785,0.23130802441652312,0.0930984616279602,0.07937531624198697,12.335314172087237,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.001418207073584199,2.4791746139526367,2.480125904083252,0.9821767173707485,0.005401274128408865,1.2981136292219162,0.2931646954290759,0.29866887629032135,0.1795070873545561,12.447027786867693,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.43699685391038656,-1.000546932220459,-1.0006884336471558,0.7747754752635956,0.058490171263640194,0.2900889264419675,0.17924970945990087,0.03397487476468086,0.02680954816596457,12.246252439916134,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,0.8690593559294939,-1.5305461883544922,-1.5395848751068115,0.7084750160574913,0.065593725110535,0.36512121092528105,0.1975168402960632,0.049283716827631,0.041151241313050604,12.158463388914242,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,1.3084656209684908,-1.6075340509414673,-1.5352838039398193,0.6835318878293037,0.07480395067854125,0.25694860937073827,0.11270696731231732,0.03814683109521866,0.038936443169290964,12.291423994116485,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,1.7429083760362118,-2.139862060546875,-2.0793771743774414,0.6471562832593918,0.08616213261321395,0.19601246248930693,0.12020882289068463,0.019202377647161484,0.025823011231531294,12.086755958851427,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,2.1761614019051194,-2.534047842025757,-2.4839212894439697,0.7050448060035706,0.04309611131081313,0.2270747146103531,0.12630548040753056,0.03467264026403427,0.037427316670982966,12.270489614922553,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,2.6070323626045138,-1.6558499336242676,-1.5982798337936401,0.6933501549065113,0.08393741199642742,0.12220262037590146,0.06107468221086527,0.00982309877872467,0.013455913221031542,12.079654044937342,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",70,3.038933440344408,-2.108987808227539,-2.047431230545044,0.6661894023418427,0.0880412489694239,0.11342712515033782,0.06763335252663888,0.006858959794044495,0.009762729972789081,12.166121675167233,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",80,3.47598042502068,-2.5105679035186768,-2.4381046295166016,0.6352470405399799,0.04752946234614613,0.28591507859528065,0.11884203390274674,0.039172131568193436,0.02941863169930098,12.209117956925184,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.0013560799416154623,2.4605331420898438,2.462231159210205,0.9817800112068653,0.0030185148005402278,1.3060883469879627,0.3026078427217335,0.3012266010046005,0.1815865864241633,12.603153039002791,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.4399991352111101,-0.5298826098442078,-0.5490257143974304,0.8070408925414085,0.05950637214733269,0.2695957152172923,0.17011969541181504,0.02614230290055275,0.03238311750950299,12.704335467191413,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,0.8721140038687736,-1.3793206214904785,-1.3437446355819702,0.7170763835310936,0.07569959495052081,0.19289464107714593,0.10044763689225858,0.017814598977565765,0.018907453921128217,12.83950568200089,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,1.3025722336024046,-2.28745436668396,-2.274547576904297,0.6980628035962582,0.06773038481287333,0.2952977903187275,0.1748357993586604,0.04525546729564667,0.042689937999250985,12.609139477834105,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,1.7348001678474247,-2.397954225540161,-2.3408455848693848,0.6737053543329239,0.08676845642644146,0.15269245905801654,0.09939968023955863,0.010251827538013458,0.012675066705884615,12.76361763314344,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,2.167869968339801,-1.790283441543579,-1.6882596015930176,0.6798370443284512,0.06146685320810478,0.2090038035530597,0.09720026045165735,0.02005765214562416,0.014490261370816129,12.802621068898588,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,2.5999056317377836,-1.4240280389785767,-1.3825451135635376,0.6812327802181244,0.0617676346545634,0.16255414206534624,0.10737495983402306,0.019677765667438507,0.025608363802346285,12.665601649088785,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",70,3.0323284207843244,-1.9012399911880493,-1.8563722372055054,0.6035140790045261,0.05500849076752055,0.1076094158925116,0.04425401325174747,0.005277249962091446,0.005267634751838362,12.877700879937038,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.00126059097237885,2.5185258388519287,2.520378351211548,0.9820863753557205,0.0048579028906396825,1.2963178679347038,0.2933059232881976,0.29931654781103134,0.1801776500182948,12.8802286721766,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.4393509109504521,-0.671017050743103,-0.5814245343208313,0.7578065916895866,0.07704611074387453,0.2358697997406125,0.12025878656590047,0.023843754082918167,0.022085622647001986,12.489072914002463,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,0.8709687113296241,-1.694675326347351,-1.5478017330169678,0.6822119727730751,0.0821458730041992,0.27154096867889166,0.16719436349442898,0.04642914608120918,0.0525131599241449,12.857435368932784,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,1.3023632750846446,-2.0144455432891846,-1.9882804155349731,0.7018444687128067,0.04994746998436156,0.18648436781950295,0.13092218038890985,0.018939167261123657,0.02286857843768079,12.691757948836312,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,1.7384464570786804,-2.3708839416503906,-2.249788284301758,0.7132603265345097,0.0459097989930154,0.2766930740326643,0.1388292937101571,0.04604111611843109,0.044756754319142306,12.829109919024631,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,2.1737114903517067,-2.367262125015259,-2.1768910884857178,0.6850602347403765,0.0835929316154266,0.20129008824005723,0.12554360869362782,0.02669866755604744,0.031610574390105055,12.827194378012791,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,2.6065414706245065,-0.8825877904891968,-0.8341261148452759,0.6785561218857765,0.06225565448082482,0.25581270968541503,0.11673563778755108,0.030630238354206085,0.027639074640801575,12.812904544174671,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",70,3.0427015263121575,-1.7757877111434937,-1.6199913024902344,0.6459879092872143,0.0717107921147389,0.16067866166122258,0.09130363728641197,0.013066068291664124,0.0160782742034978,12.810403718845919,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.0012726939748972654,2.5575685501098633,2.5589418411254883,0.9817499592900276,0.0045550778552189905,1.2933147735893726,0.29736018449070306,0.2971441429108381,0.18287682807710598,12.338738089893013,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.43275923701003194,-0.4872037470340729,-0.46289247274398804,0.7719661332666874,0.0645011416638657,0.200195771176368,0.08490746519472354,0.013314232230186462,0.0142774387653314,12.126065816963091,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,0.8656863104552031,-0.5671861171722412,-0.5636565685272217,0.6935681141912937,0.07830265677939828,0.21348727075383067,0.0899435459255553,0.01927187666296959,0.015649195865371845,12.395660446025431,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,1.2980717064347118,-1.4005612134933472,-1.3757978677749634,0.6791380681097507,0.07771149930387201,0.36358275124803185,0.14891758559758986,0.048737719655036926,0.032017906188848554,12.2596564181149,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,1.7285774112679064,-1.4343371391296387,-1.4370315074920654,0.6158773601055145,0.06942388132888767,0.17139986460097134,0.10220214163777452,0.011351704597473145,0.012731352181815323,12.176258407067508,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,2.1628858011681587,-2.0104525089263916,-1.927751064300537,0.6486771702766418,0.06984602454784587,0.28816130850464106,0.1699795050076622,0.03389919921755791,0.03862562699658173,12.206566552864388,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,2.5985229993239045,-3.040097951889038,-2.9418210983276367,0.7134456411004066,0.039957389105880765,0.40118418727070093,0.2360164833120648,0.09697363153100014,0.08198858545987518,12.011812212876976,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",70,3.0302535500377417,-2.873419761657715,-2.842287063598633,0.618389219045639,0.05258447690959609,0.18595365062355995,0.09948880835528766,0.013912603259086609,0.011243880343467688,12.485046749003232,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",80,3.4625348919071257,-2.1501259803771973,-2.08314847946167,0.629948191344738,0.058070161108835706,0.16923087579198182,0.07813071400163281,0.013633165508508682,0.01692108234559256,12.098707507131621,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=3]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",90,3.892106272513047,-2.0034725666046143,-1.9554013013839722,0.6259999144822359,0.07297113600795266,0.1508251130580902,0.09148866428517047,0.018472835421562195,0.026622867323245113,12.30661931890063,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.0014932057820260525,2.4532158374786377,2.452953338623047,0.9823905862867832,0.004067972628080914,1.3027199767529964,0.2862111187100476,0.27020955085754395,0.19379752275582765,39.598835988901556,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.7024954278022051,-1.1580220460891724,-1.128312587738037,0.7174655646085739,0.05632215426955329,0.19322118815034628,0.0873484394547172,0.018007081001996994,0.021640312538124602,31.034426297992468,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,1.4131844381336123,-1.0812324285507202,-1.0013927221298218,0.691553458571434,0.04635422625685483,0.20212431764230132,0.09489762848891682,0.019250143319368362,0.016403585808530064,31.231221752939746,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,2.11551366513595,-1.2967989444732666,-1.302390694618225,0.6945763789117336,0.05688100233556673,0.14847650658339262,0.05792805690698096,0.010572928935289383,0.00882296470866002,35.87023503193632,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,2.814323044149205,-1.6557670831680298,-1.571014165878296,0.6127882525324821,0.05132144599914712,0.12421584222465754,0.04985881579006071,0.006726417690515518,0.007791239005945152,32.93494027899578,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,3.526348799234256,-2.186762571334839,-2.09531569480896,0.5930475853383541,0.04662084710710312,0.17153690848499537,0.08826366050012355,0.015715964138507843,0.017351215951354162,37.094822665909305,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,4.265317003475502,-1.3713374137878418,-1.2546499967575073,0.6270671337842941,0.0603228903313599,0.19875433901324868,0.10096736939828434,0.012570735067129135,0.00620518318953292,37.50581328198314,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",70,4.985811714781448,-1.5612221956253052,-1.4663262367248535,0.5668962821364403,0.054031537546186593,0.10263210814446211,0.062068776180508156,0.0033620111644268036,0.004656249481146825,36.38077835389413,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",80,5.698883796809241,-2.201185703277588,-2.1312973499298096,0.561095766723156,0.03551589949910945,0.1485748200211674,0.08800988878036772,0.010617077350616455,0.012778029275756385,34.64061658619903,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",90,6.411492015002295,-2.1752829551696777,-2.119675636291504,0.5517273731529713,0.022326026880942366,0.13988520856946707,0.10461843241135653,0.003703467547893524,0.002807851071393703,40.16735121398233,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",100,7.1314105277415365,-2.331037998199463,-2.2687525749206543,0.564392551779747,0.023679182266415785,0.20803668885491788,0.12171470881265287,0.01407148689031601,0.01353736542776754,37.98940533492714,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",110,7.840229024644941,-2.373227834701538,-2.389312505722046,0.5458336062729359,0.029589154999839364,0.1314997891895473,0.11400052431347558,0.004961911588907242,0.004427846502355842,34.45377443009056,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",120,8.554316423367709,-2.210845708847046,-2.1429905891418457,0.5266089309006929,0.02689635919721354,0.11980731890071183,0.10704496773846661,0.004048921167850494,0.004515346033442558,33.034892588853836,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",130,9.263120256597176,-2.346467971801758,-2.3166251182556152,0.5315208025276661,0.03571919634358823,0.11782705155201256,0.12237519221643167,0.002403680235147476,0.002949439334062817,36.86081507592462,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",0,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",140,9.974852834595367,-2.0758650302886963,-2.041320323944092,0.5451056212186813,0.025540037792859693,0.1360536403954029,0.12751432694897205,0.0071103982627391815,0.007583077950490184,29.75931528909132,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.00154978409409523,2.3851330280303955,2.385188102722168,0.9825422689318657,0.005052533260362698,1.3036085478961468,0.2996966091598871,0.26678129471838474,0.19286568804706458,42.073397391010076,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.717317767906934,-0.7542706727981567,-0.6581965684890747,0.8069419786334038,0.061289369130057836,0.38488876819610596,0.23508955690106115,0.07287591695785522,0.07791081743820896,32.010897687170655,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,1.432479523587972,-1.2991684675216675,-1.2956873178482056,0.787871815264225,0.04340100139349864,0.2257571965456009,0.10234886709147191,0.01764528453350067,0.013232062046141151,33.49524351907894,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,2.1567623019218445,-1.3351497650146484,-1.359972357749939,0.7102390564978123,0.08306843374485487,0.1565711898729205,0.03636849586731535,0.012903090566396713,0.01744240552922347,27.413218140834942,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,2.883461288176477,-1.0777382850646973,-1.0916080474853516,0.6735759414732456,0.052192820890192115,0.10603593243286014,0.05369687934095245,0.008533243089914322,0.010622117670688406,26.433869637083262,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,3.6100501315668225,-1.1569968461990356,-1.100102424621582,0.6562070585787296,0.06514833694616011,0.14890044438652694,0.08877797943044961,0.016197487711906433,0.024910999079838042,26.71456341492012,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",1,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,4.336419354658574,-1.2576874494552612,-1.2383029460906982,0.6703527271747589,0.05713301192224929,0.1243767230771482,0.07363300713394008,0.008164804428815842,0.011230447441007652,26.371566546149552,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.0017307321541011333,2.5550854206085205,2.554769515991211,0.982236884534359,0.004578014700196238,1.3019271455705166,0.3046565460462527,0.2641863785684109,0.1916199453304176,35.853541614022106,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.7127243503928185,-2.053781509399414,-2.0416629314422607,0.7212497033178806,0.06763339765689777,0.2356994329020381,0.07706228317759338,0.024499382823705673,0.018561898190919073,24.170752845006064,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,1.4152143623214215,-1.521474838256836,-1.4545010328292847,0.6796068660914898,0.051134662929081455,0.22817415092140436,0.21086604632163752,0.04330885037779808,0.07661566122810885,23.39418788300827,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,2.09495870443061,-1.7097371816635132,-1.6538190841674805,0.6196135692298412,0.06786726207654604,0.19809926697053015,0.12368667517009672,0.010687515139579773,0.008074901159910703,22.324740656884387,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",2,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,2.7756316082086414,-1.3963854312896729,-1.2948054075241089,0.6394631415605545,0.0678101626721654,0.1402248260565102,0.05967558855847592,0.01255849003791809,0.01715926298787989,21.6838207480032,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.001958389999344945,2.4756853580474854,2.475283622741699,0.9816592484712601,0.005065016140020064,1.294674750417471,0.2980264042960177,0.263514855876565,0.19104614749492413,27.834783310769126,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.6864458164200187,-0.48586320877075195,-0.5523266196250916,0.7304656691849232,0.06325904060935744,0.1467542969621718,0.04618290268593674,0.01253809779882431,0.017065998800378027,19.79299689619802,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,1.366871528327465,-1.568092703819275,-1.4228096008300781,0.6979073770344257,0.07543639760843077,0.18525699200108647,0.05890729661258505,0.01750839129090309,0.01589034897761536,19.46533086593263,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,2.042282310081646,-1.5318254232406616,-1.488957405090332,0.6608881726861,0.074213660508641,0.19376851106062531,0.12959929792821812,0.01843741536140442,0.015035357269595864,18.915985075058416,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,2.722598765278235,-1.3785008192062378,-1.2944374084472656,0.6105198580771685,0.060865922907552515,0.15526477876119316,0.08092234964945157,0.01736309379339218,0.024894473783996028,18.988416085019708,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,3.4030780487228185,-1.8677774667739868,-1.855851411819458,0.6079682782292366,0.05738679188490706,0.17757582548074424,0.10328564321491941,0.014052238315343857,0.019285487060710964,19.03647850896232,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,4.0828192450571805,-1.084210753440857,-1.0468236207962036,0.6286277547478676,0.0541181917179996,0.19054404622875154,0.1540804211443338,0.022643808275461197,0.036102440382830836,17.500680441968143,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",70,4.762646772898734,-1.4570492506027222,-1.414315938949585,0.6079342104494572,0.04565496789816307,0.19206302519887686,0.1395942904975862,0.017579026520252228,0.02986200311331484,18.63255745009519,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",3,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",80,5.4437221279367805,-1.6752084493637085,-1.6522984504699707,0.5891485810279846,0.05330681058497103,0.14101648121140897,0.09451193311337028,0.0060373395681381226,0.007389363858110253,18.465496591990814,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.0017447678837925196,2.482304096221924,2.482379198074341,0.9826051890850067,0.0041184425153441215,1.292942725121975,0.2869107383185127,0.2649214640259743,0.19257666205479412,20.658066757954657,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.6856776946224272,-1.1597435474395752,-1.1466115713119507,0.7618051841855049,0.07948159401246532,0.2265016920864582,0.09209397689153746,0.028831060975790024,0.025545582745221233,20.626513669034466,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,1.3623778757173568,-1.3309388160705566,-1.2646023035049438,0.7010523863136768,0.058998637563973506,0.19236778328195214,0.12772768884685368,0.02477821335196495,0.02641088516786106,20.40416711405851,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,2.038800355279818,-1.4447765350341797,-1.4147288799285889,0.6871902085840702,0.05568108953243745,0.31420514872297645,0.17423956518312153,0.05780986323952675,0.05862195477110654,20.28664054418914,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,2.7206326480954885,-1.145269513130188,-1.0919075012207031,0.6847514770925045,0.043159675416318925,0.2522218101657927,0.17244009514318506,0.040438439697027206,0.06250812672486772,20.377457665046677,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,3.3980393807869405,-1.3547821044921875,-1.3168516159057617,0.6323866955935955,0.05416207466621036,0.13447827799245715,0.09236907662392753,0.006169989705085754,0.007055838029245196,19.779511148110032,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,4.079447338590398,-1.882549524307251,-1.8493646383285522,0.6444345116615295,0.060201081747102786,0.15360432607121766,0.06705629425194172,0.010650970041751862,0.01266516515594883,19.82671150821261,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",70,4.757279763463885,-1.9641573429107666,-1.9327435493469238,0.6016767546534538,0.06436648889624394,0.10515025723725557,0.05351549945532243,0.0042519159615039825,0.005824412153599169,20.587013541022316,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",80,5.437504768138751,-1.8403246402740479,-1.8418220281600952,0.5873392783105373,0.05754044669796245,0.11420246842317283,0.0468882304828797,0.005167961120605469,0.005286494307372372,20.434767490951344,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",90,6.119419523049146,-1.8454947471618652,-1.8770651817321777,0.6338125392794609,0.05351143663215476,0.2071705332491547,0.13348779552763218,0.02678763121366501,0.026752613301545315,20.315407810965553,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",4,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",100,6.8022180332336575,-1.5993324518203735,-1.6060585975646973,0.6619707494974136,0.059793354542234646,0.12968298699706793,0.08197962376218894,0.003952424973249435,0.004491902897390961,19.974477075971663,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.0016645679716020823,2.488076686859131,2.487600088119507,0.9825421273708344,0.005280073408367883,1.3007190972566605,0.30850442728445115,0.26721993274986744,0.19506041339691296,17.43785953009501,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.6806828235276043,-0.6842416524887085,-0.6039775609970093,0.7495699636638165,0.07371362659206329,0.2209975915029645,0.12412827839191096,0.01802310347557068,0.017428044739588033,17.979205660056323,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,1.3628526797983795,-2.0520071983337402,-1.9993400573730469,0.7074654139578342,0.08615102389329825,0.16775597911328077,0.1021723551644889,0.00931764766573906,0.013665619588695398,17.97848652303219,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,2.042408745503053,-2.4103972911834717,-2.3351895809173584,0.6804728247225285,0.04945527836379356,0.17780973762273788,0.0807607338795538,0.0074753537774086,0.00596212248849409,17.904145952081308,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,2.7235354548320174,-1.2754356861114502,-1.2475601434707642,0.6208148263394833,0.06473494304516621,0.15674008382484317,0.0914216132786304,0.0077628158032894135,0.007276464459786364,17.966164813842624,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,3.401964229531586,-1.3952744007110596,-1.385056972503662,0.5966635569930077,0.04971633223077473,0.18799693486653268,0.07635799651050937,0.01848806068301201,0.018637706777393466,17.981892651878297,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",5,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,4.0863586405757815,-1.6417053937911987,-1.652308464050293,0.5773003734648228,0.05379777589525201,0.16445533651858568,0.0794607170816193,0.008436817675828934,0.008334864718950397,18.077413527993485,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.001681310124695301,2.6131486892700195,2.6128792762756348,0.9815969280898571,0.005283727362488553,1.306189849972725,0.302048292217566,0.2666714023798704,0.19310799274336335,18.12900038389489,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.6842872072011232,-1.3675004243850708,-1.338904619216919,0.7489912882447243,0.08114322820892653,0.1882034034933895,0.09004569137133646,0.013930100947618484,0.014472654080832321,18.101998070953414,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,1.3636914738453925,-0.8898687958717346,-0.8489472270011902,0.7087110914289951,0.07195982225231908,0.18008042266592383,0.0918511521006578,0.01667206734418869,0.02283458282404929,18.1445656910073,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,2.0402504159137607,-1.1748193502426147,-1.117850661277771,0.6650948151946068,0.07240668550660397,0.12617798638530076,0.057865298197157994,0.007026348263025284,0.010608430134708781,18.067364221904427,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,2.721215669764206,-2.176011323928833,-2.148658514022827,0.6739446315914392,0.07127174249668404,0.1270648103673011,0.058253396952319875,0.0076799094676971436,0.010463387627597045,18.090965571114793,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,3.4004894443787634,-1.204982876777649,-1.2035377025604248,0.6828262805938721,0.07502052161275634,0.22742217360064387,0.11232634712968087,0.02145269140601158,0.01867579954869749,17.869414578191936,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,4.0821951143443584,-1.6703550815582275,-1.6110923290252686,0.6427475549280643,0.07390448592173744,0.13059600768610835,0.050431389211128656,0.006279762834310532,0.005936530323178716,18.060967105906457,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",6,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",70,4.760256146080792,-1.2761050462722778,-1.2350555658340454,0.6574927642941475,0.07076921317145181,0.1300790694076568,0.061754559487583745,0.007559932768344879,0.007233615201784025,18.133580010849983,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.0019245960284024477,2.5121355056762695,2.5115485191345215,0.9814451634883881,0.005029637921738589,1.2942898236215115,0.2854943019135331,0.2633541766554117,0.19051483028208757,18.096528518013656,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.6843716821167618,-1.4804222583770752,-1.4342468976974487,0.7117874398827553,0.05558464921976554,0.1673837467096746,0.06427409839037322,0.013574954122304916,0.01690858352338916,17.934704002924263,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,1.3660770589485765,-1.4952424764633179,-1.464630126953125,0.6917431689798832,0.059948840433815466,0.16753870295360684,0.07238304189922144,0.01864459365606308,0.02297200936169665,17.74099868698977,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,2.0454142584931105,-0.832551896572113,-0.8473097085952759,0.6307672671973705,0.07953903286022282,0.1450437696184963,0.08827432620669377,0.009166188538074493,0.01093163210538255,17.85716157988645,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,2.7224825411103666,-1.738624095916748,-1.672617793083191,0.6224314235150814,0.06658743411615704,0.11679229908622801,0.05563579322805407,0.004815489053726196,0.0061509230910214265,18.110491706058383,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,3.4023848299402744,-1.6838231086730957,-1.6067081689834595,0.5998384729027748,0.06371420369069669,0.14002682897262275,0.10105979539241229,0.005876727402210236,0.009157060429985183,18.11329792183824,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,4.082016228931025,-1.5614556074142456,-1.5448780059814453,0.5923437662422657,0.0762352825477488,0.14396208967082202,0.09614116890185605,0.006235715001821518,0.00791864148937588,18.05767731787637,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",7,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",70,4.762870567850769,-1.7071613073349,-1.660185694694519,0.6539289765059948,0.06090556028913937,0.24448030907660723,0.19452191966450683,0.03788572549819946,0.057277890828484085,18.051171666942537,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.0016783250030130148,2.4133691787719727,2.4135947227478027,0.9812025353312492,0.0052473983107326734,1.2901768274605274,0.2954640944740445,0.264537550508976,0.19453981173886858,18.210220070090145,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.6818312832619995,-1.6632747650146484,-1.5690537691116333,0.7400244548916817,0.06805529358963246,0.36538967583328485,0.18127882617876093,0.07684949785470963,0.06764613070528484,18.05973910400644,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,1.3655502947513014,-1.0868861675262451,-0.96536785364151,0.7018985897302628,0.06783249617299002,0.21650920435786247,0.15463753463264296,0.02748006582260132,0.03674590514030343,18.138401943957433,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,2.04213078902103,-1.6182758808135986,-1.585681438446045,0.6203039102256298,0.08701480283291603,0.23544238414615393,0.13301676819862626,0.0220889113843441,0.024815719036440243,17.932750392938033,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,2.7191198507789522,-1.8333443403244019,-1.7711873054504395,0.665409829467535,0.054811776338854465,0.1308859724085778,0.08285563911485662,0.007103480398654938,0.009581225312506082,18.10980583098717,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,3.4073042916134,-1.593385100364685,-1.5964112281799316,0.6149021182209253,0.08501008991062038,0.131173029076308,0.06192435325802535,0.012884680181741714,0.02018749276824136,18.006361166015267,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,4.083775239996612,-1.1397602558135986,-1.1677500009536743,0.6408265307545662,0.07448812268449162,0.12594159715808928,0.06505101100421459,0.010805614292621613,0.012020775633116746,17.968987199943513,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",8,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",70,4.761458551045507,-1.4131278991699219,-1.4166741371154785,0.6314652785658836,0.0634473330656754,0.2307615987956524,0.12261641356920544,0.024779118597507477,0.023651440798332386,17.948685925919563,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",0,0.001777919940650463,2.5592575073242188,2.559488534927368,0.9815667383372784,0.005652523239993636,1.2952406741678715,0.3066530633416674,0.26459362730383873,0.1946710695128598,17.964654630981386,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",10,0.6813283036462963,-1.2093634605407715,-1.149142861366272,0.7651932872831821,0.06269241002286008,0.2769696833565831,0.21329560741639575,0.03259619325399399,0.04025032249590836,18.111064427066594,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",20,1.359728749608621,-0.9690638780593872,-0.9750997424125671,0.7239665500819683,0.07819833441765839,0.20630072196945548,0.12048353125514341,0.01702224463224411,0.01714272645355643,17.93946688901633,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",30,2.039457404986024,-1.6196563243865967,-1.5403140783309937,0.6655522882938385,0.07259224127198062,0.18868627375923097,0.1080004996797513,0.010545101016759872,0.008785804116022953,18.070253426907584,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",40,2.7177337089087814,-1.9451534748077393,-2.002465009689331,0.697479359805584,0.04770613680975689,0.24404714116826653,0.15624964204911593,0.01756066456437111,0.01535308021911854,17.7257501129061,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",50,3.3983599776402116,-1.560316562652588,-1.5185068845748901,0.6558192931115627,0.055074325014714424,0.20447778096422553,0.15160089891816125,0.013061989098787308,0.016976929153502733,18.018654617015272,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",60,4.076181025244296,-1.623580813407898,-1.5725058317184448,0.6478488631546497,0.0674925536003897,0.23035594238899648,0.1560781744826032,0.01729176566004753,0.017917294516852745,17.959571149898693,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]
["sbi: maximum likelihood on test set","nre_lampe[layers=5]","two_moons[n_per_ref=1024,ref_size=16,seed=42,test_size=256,train_size=4096]",9,"Iteration","Benchmark amortized simulation-based inference (SBI) algorithms.\n\n    Datasets:\n        - train/test: parameter-observation pairs from the prior-simulator\n            joint distribution :math:`p(\\theta, x)=p(\\theta)p(x|\\theta)`.\n        - reference (optional): observations math:`x_ref` and corresponding\n            samples from the reference posterior :math:`p(\\theta|x_ref)`\n            (if available).\n\n    Solvers: amortized SBI algorithms trained on the joint to approximate\n        the posterior :math:`p(\\theta|x)` for any observation :math:`x`.\n\n    Metrics:\n        - expected negative log likelihood (NLL) on test (stopping criterion)\n            and train datasets.\n        - C2ST, EMD, MMD on reference dataset (optional).\n    ","Neural ratio estimation (NRE).\n\n    The solver trains a classifier to discriminate between pairs sampled from the joint\n    distribution :math:`p(\\theta, x)` and the product of marginals :math:`p(\\theta)\n    p(x)`.\n\n    Implementated with the :mod:`lampe` package.\n    ",70,4.754807800287381,-1.6819632053375244,-1.5915734767913818,0.7001911960542202,0.06432518206218302,0.2155419117771089,0.1805868653389225,0.011797484010457993,0.00946918947972395,17.971112842904404,null,"Linux","x86_64","4.18.0-394.el8.x86_64","#1 SMP Tue May 31 16:19:11 UTC 2022",10,"AMD EPYC 7742 64-Core Processor",503,null,["1.25.0","blas=mkl_rt, lapack=mkl_rt"],"1.11.1",null]