Likelihood-free Model Choice

TL;DR

This chapter reviews likelihood-free model choice methods, focusing on ABC and the innovative use of random forests to improve model selection accuracy and estimate posterior probabilities.

Contribution

It highlights the application of random forests in ABC for model choice, addressing pitfalls and proposing a robust alternative to traditional methods.

Findings

Random forests improve summary statistic aggregation in ABC.

The method provides more reliable posterior model probabilities.

Potential pitfalls of ABC posterior probabilities are discussed.

Abstract

This document is an invited chapter covering the specificities of ABC model choice, intended for the incoming Handbook of ABC by Sisson, Fan, and Beaumont (2017). Beyond exposing the potential pitfalls of ABC based posterior probabilities, the review emphasizes mostly the solution proposed by Pudlo et al. (2016) on the use of random forests for aggregating summary statistics and and for estimating the posterior probability of the most likely model via a secondary random fores.