Mining gold from implicit models to improve likelihood-free inference

TL;DR

This paper introduces advanced simulation-based inference methods that leverage additional information from simulators, such as joint likelihood ratios and scores, to enhance likelihood-free inference in high-dimensional problems, outperforming traditional approaches.

Contribution

The authors develop novel inference techniques that incorporate joint likelihood ratio and score information from simulators, improving sample efficiency and inference accuracy over existing methods.

Findings

More sample-efficient inference methods demonstrated.

Higher-fidelity results compared to traditional approaches.

Effective in high-dimensional inverse problems.

Abstract

Simulators often provide the best description of real-world phenomena. However, they also lead to challenging inverse problems because the density they implicitly define is often intractable. We present a new suite of simulation-based inference techniques that go beyond the traditional Approximate Bayesian Computation approach, which struggles in a high-dimensional setting, and extend methods that use surrogate models based on neural networks. We show that additional information, such as the joint likelihood ratio and the joint score, can often be extracted from simulators and used to augment the training data for these surrogate models. Finally, we demonstrate that these new techniques are more sample efficient and provide higher-fidelity inference than traditional methods.