On computational tools for Bayesian data analysis

TL;DR

This paper reviews computational methods for Bayesian data analysis, highlighting recent innovations like MCMC, SMC, and ABC, which enhance Bayesian inference and model choice capabilities.

Contribution

It provides a comprehensive overview of recent computational techniques in Bayesian analysis, emphasizing their practical applications and advancements.

Findings

Monte Carlo Markov Chain methods enable efficient Bayesian inference.

Sequential Monte Carlo techniques expand Bayesian modeling options.

Approximate Bayesian Computation opens new avenues for complex models.

Abstract

While Robert and Rousseau (2010) addressed the foundational aspects of Bayesian analysis, the current chapter details its practical aspects through a review of the computational methods available for approximating Bayesian procedures. Recent innovations like Monte Carlo Markov chain, sequential Monte Carlo methods and more recently Approximate Bayesian Computation techniques have considerably increased the potential for Bayesian applications and they have also opened new avenues for Bayesian inference, first and foremost Bayesian model choice.