Likelihood-free Model Choice
This is an incremental review chapter for researchers in computational statistics, summarizing existing methods without introducing new findings.
The chapter reviews ABC model choice, highlighting the pitfalls of ABC-based posterior probabilities and focusing on the solution from Pudlo et al. (2016) that uses random forests to aggregate summary statistics and estimate posterior probabilities.
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.