Approximate Methods for Bayesian Computation

TL;DR

This paper reviews recent algorithmic advances in Bayesian computation, focusing on approximate methods that address challenges posed by big data and complex models, and compares their performance through experiments.

Contribution

It provides a comprehensive review and comparison of modern approximate algorithms for Bayesian inference in the context of large, complex datasets.

Findings

Modern algorithms improve computational efficiency

Approximate methods offer scalable solutions

Performance varies across different models

Abstract

Rich data generating mechanisms are ubiquitous in this age of information and require complex statistical models to draw meaningful inference. While Bayesian analysis has seen enormous development in the last 30 years, benefitting from the impetus given by the successful application of Markov chain Monte Carlo (MCMC) sampling, the combination of big data and complex models conspire to produce significant challenges for the traditional MCMC algorithms. We review modern algorithmic developments addressing the latter and compare their performance using numerical experiments.