BibTeX

@article{2207.05636v1,
Author        = {Justine Zeghal and François Lanusse and Alexandre Boucaud and Benjamin Remy and Eric Aubourg},
Title         = {Neural Posterior Estimation with Differentiable Simulators},
Eprint        = {2207.05636v1},
ArchivePrefix = {arXiv},
PrimaryClass  = {astro-ph.IM},
Abstract      = {Simulation-Based Inference (SBI) is a promising Bayesian inference framework
that alleviates the need for analytic likelihoods to estimate posterior
distributions. Recent advances using neural density estimators in SBI
algorithms have demonstrated the ability to achieve high-fidelity posteriors,
at the expense of a large number of simulations ; which makes their application
potentially very time-consuming when using complex physical simulations. In
this work we focus on boosting the sample-efficiency of posterior density
estimation using the gradients of the simulator. We present a new method to
perform Neural Posterior Estimation (NPE) with a differentiable simulator. We
demonstrate how gradient information helps constrain the shape of the posterior
and improves sample-efficiency.},
Year          = {2022},
Month         = {Jul},
Url           = {http://arxiv.org/abs/2207.05636v1},
File          = {2207.05636v1.pdf}
}