Result on sbi benchmark

0.0e+02.0e+04.0e+06.0e+08.0e+02.7e+02.8e+02.9e+03.0e+03.1e+03.2e+03.3e+03.4e+03.5e+03.6e+0
sbi: maximum likelihood on test setData: simulated[seed=42,test_size=256,train_size=1024]Time [sec]F(x)
Solvers (Click on a solver to hide it or double click to hide all the others)
fmpe_lampe[layers=3]
Flow matching posterior estimator (FMPE) [1,2]. The solver trains a regression network to approximate a vector field inducing a time-continuous normalizing flow between the posterior distribution and a standard Gaussian distribution. Implemented with the :mod:`lampe` package.
fmpe_lampe[layers=5]
Flow matching posterior estimator (FMPE) [1,2]. The solver trains a regression network to approximate a vector field inducing a time-continuous normalizing flow between the posterior distribution and a standard Gaussian distribution. Implemented with the :mod:`lampe` package.
npe_lampe[flow=maf,transforms=1]
Neural posterior estimation (NPE) [1,2]. The solver trains a parametric conditional distribution :math:`q_\phi(\theta | x)` to approximate the posterior distribution :math:`p(\theta | x)` of parameters given observations. Implementated with the :mod:`lampe` package.
npe_lampe[flow=maf,transforms=3]
Neural posterior estimation (NPE) [1,2]. The solver trains a parametric conditional distribution :math:`q_\phi(\theta | x)` to approximate the posterior distribution :math:`p(\theta | x)` of parameters given observations. Implementated with the :mod:`lampe` package.
npe_lampe[flow=maf,transforms=5]
Neural posterior estimation (NPE) [1,2]. The solver trains a parametric conditional distribution :math:`q_\phi(\theta | x)` to approximate the posterior distribution :math:`p(\theta | x)` of parameters given observations. Implementated with the :mod:`lampe` package.
npe_lampe[flow=nsf,transforms=1]
Neural posterior estimation (NPE) [1,2]. The solver trains a parametric conditional distribution :math:`q_\phi(\theta | x)` to approximate the posterior distribution :math:`p(\theta | x)` of parameters given observations. Implementated with the :mod:`lampe` package.
npe_lampe[flow=nsf,transforms=3]
Neural posterior estimation (NPE) [1,2]. The solver trains a parametric conditional distribution :math:`q_\phi(\theta | x)` to approximate the posterior distribution :math:`p(\theta | x)` of parameters given observations. Implementated with the :mod:`lampe` package.
npe_lampe[flow=nsf,transforms=5]
Neural posterior estimation (NPE) [1,2]. The solver trains a parametric conditional distribution :math:`q_\phi(\theta | x)` to approximate the posterior distribution :math:`p(\theta | x)` of parameters given observations. Implementated with the :mod:`lampe` package.
npe_sbi[flow=maf,transforms=1]
Neural posterior estimation (NPE). The solver trains a parametric conditional distribution :math:`q_\phi(\theta | x)` to approximate the posterior distribution :math:`p(\theta | x)` of parameters given observations. Implementated with the :mod:`sbi` package.
npe_sbi[flow=maf,transforms=3]
Neural posterior estimation (NPE). The solver trains a parametric conditional distribution :math:`q_\phi(\theta | x)` to approximate the posterior distribution :math:`p(\theta | x)` of parameters given observations. Implementated with the :mod:`sbi` package.
npe_sbi[flow=maf,transforms=5]
Neural posterior estimation (NPE). The solver trains a parametric conditional distribution :math:`q_\phi(\theta | x)` to approximate the posterior distribution :math:`p(\theta | x)` of parameters given observations. Implementated with the :mod:`sbi` package.
npe_sbi[flow=nsf,transforms=1]
Neural posterior estimation (NPE). The solver trains a parametric conditional distribution :math:`q_\phi(\theta | x)` to approximate the posterior distribution :math:`p(\theta | x)` of parameters given observations. Implementated with the :mod:`sbi` package.
npe_sbi[flow=nsf,transforms=3]
Neural posterior estimation (NPE). The solver trains a parametric conditional distribution :math:`q_\phi(\theta | x)` to approximate the posterior distribution :math:`p(\theta | x)` of parameters given observations. Implementated with the :mod:`sbi` package.
npe_sbi[flow=nsf,transforms=5]
Neural posterior estimation (NPE). The solver trains a parametric conditional distribution :math:`q_\phi(\theta | x)` to approximate the posterior distribution :math:`p(\theta | x)` of parameters given observations. Implementated with the :mod:`sbi` package.
nre_lampe[layers=3]
Neural ratio estimation (NRE). The solver trains a classifier to discriminate between pairs sampled from the joint distribution :math:`p(\theta, x)` and the product of marginals :math:`p(\theta) p(x)`. Implementated with the :mod:`lampe` package.
nre_lampe[layers=5]
Neural ratio estimation (NRE). The solver trains a classifier to discriminate between pairs sampled from the joint distribution :math:`p(\theta, x)` and the product of marginals :math:`p(\theta) p(x)`. Implementated with the :mod:`lampe` package.