Balancing Simulation-based Inference for Conservative Posteriors

Arnaud Delaunoy, Benjamin Kurt Miller, Patrick Forré, Christoph Weniger, Gilles Louppe

Keywords: simulation-based inference, likelihood-free inference, approximate Bayesian inference

TL;DR: We extend balancing to any algorithm that provides a posterior density and provide an alternative interpretation of the balancing condition in terms of the χ2 divergence.

Abstract: Conservative inference is a major concern in simulation-based inference. It has been shown that commonly used algorithms can produce overconfident posterior approximations. Balancing has empirically proven to be an effective way to mitigate this issue. However, its application remains limited to neural ratio estimation. In this work, we extend balancing to any algorithm that provides a posterior density. In particular, we introduce a balanced version of both neural posterior estimation and contrastive neural ratio estimation. We show empirically that the balanced versions tend to produce conservative posterior ap- proximations on a wide variety of benchmarks. In addition, we provide an alternative interpretation of the balancing condition in terms of the χ2 divergence.