Neural Methods for Amortized Inference

TL;DR

This paper reviews recent neural network-based amortized inference methods, highlighting their ability to perform rapid statistical inference after initial training, and discusses their applications, software, and future research directions.

Contribution

It provides a comprehensive overview of neural amortized inference techniques, including recent advances, applications, and software tools, emphasizing their advantages over traditional methods.

Findings

Neural methods enable fast inference after training.

Amortized inference improves efficiency over MCMC.

The paper discusses software and future research directions.

Abstract

Simulation-based methods for statistical inference have evolved dramatically over the past 50 years, keeping pace with technological advancements. The field is undergoing a new revolution as it embraces the representational capacity of neural networks, optimization libraries and graphics processing units for learning complex mappings between data and inferential targets. The resulting tools are amortized, in the sense that, after an initial setup cost, they allow rapid inference through fast feed-forward operations. In this article we review recent progress in the context of point estimation, approximate Bayesian inference, summary-statistic construction, and likelihood approximation. We also cover software, and include a simple illustration to showcase the wide array of tools available for amortized inference and the benefits they offer over Markov chain Monte Carlo methods. The article concludes with an overview of relevant topics and an outlook on future research directions.