Machine Learning and the Future of Bayesian Computation

TL;DR

This paper explores how machine learning techniques can enhance Bayesian computation, addressing challenges in posterior inference for complex, high-dimensional models through methods like normalizing flows, coresets, and variational inference.

Contribution

It discusses innovative machine learning-based approaches to improve Bayesian posterior computation, highlighting future research directions.

Findings

Normalizing flows can improve posterior sampling efficiency.

Bayesian coresets reduce data size for faster inference.

Distributed and variational methods offer scalable solutions.

Abstract

Bayesian models are a powerful tool for studying complex data, allowing the analyst to encode rich hierarchical dependencies and leverage prior information. Most importantly, they facilitate a complete characterization of uncertainty through the posterior distribution. Practical posterior computation is commonly performed via MCMC, which can be computationally infeasible for high dimensional models with many observations. In this article we discuss the potential to improve posterior computation using ideas from machine learning. Concrete future directions are explored in vignettes on normalizing flows, Bayesian coresets, distributed Bayesian inference, and variational inference.