Keywords: simulation, inference, posterior
TL;DR: We use score modeling to do simulation-based inference.
Abstract: Neural Posterior Estimation methods for simulation-based inference can be ill-suited for dealing with posterior distributions obtained by conditioning on multiple observations, as they may require a large number of simulator calls to yield accurate approximations. Neural Likelihood Estimation methods can naturally handle multiple observations, but require a separate inference step, which may affect their efficiency and performance. We introduce a new method for simulation-based inference that enjoys the benefits of both approaches. We propose to model the scores for the posterior distributions induced by individual observations, and introduce a sampling algorithm that combines the learned scores to approximately sample from the target efficiently.
Student Paper: Yes
Community Implementations: [![CatalyzeX](/images/catalyzex_icon.svg) 1 code implementation](https://www.catalyzex.com/paper/arxiv:2209.14249/code)