Likelihood-free Model Choice
Methodology
2016-09-19 v3 Computation
Machine Learning
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.
Cite
@article{arxiv.1503.07689,
title = {Likelihood-free Model Choice},
author = {Jean-Michel Marin and Pierre Pudlo and Arnaud Estoup and Christian P. Robert},
journal= {arXiv preprint arXiv:1503.07689},
year = {2016}
}
Comments
21 pages, 9 figures, 2 tables