Score Modeling for 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.