Approximate Bayesian Computational methods

TL;DR

This survey reviews recent advancements in approximate Bayesian computational methods, highlighting improvements and extensions that address calibration issues and expand their applicability across various fields.

Contribution

It provides a comprehensive overview of recent developments and extensions in ABC methods, emphasizing solutions to calibration challenges and broader application potential.

Findings

Recent improvements enhance calibration stability.

Extensions expand ABC applicability beyond genetics.

Survey highlights ongoing methodological innovations.

Abstract

Also known as likelihood-free methods, approximate Bayesian computational (ABC) methods have appeared in the past ten years as the most satisfactory approach to untractable likelihood problems, first in genetics then in a broader spectrum of applications. However, these methods suffer to some degree from calibration difficulties that make them rather volatile in their implementation and thus render them suspicious to the users of more traditional Monte Carlo methods. In this survey, we study the various improvements and extensions made to the original ABC algorithm over the recent years.