# Likelihood-free Model Choice

arXiv: https://arxiv.org/abs/1503.07689 · HTML: https://mlsanity.com/paper/1503.07689/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Mar 2015.

## Built on by (most cited first, 3 of 3)

- [JANA: Jointly Amortized Neural Approximation of Complex Bayesian Models](https://mlsanity.com/paper/2302.09125/) (2023)
- [A Deep Learning Method for Comparing Bayesian Hierarchical Models](https://mlsanity.com/paper/2301.11873/) (2023)
- [Likelihood-free Model Choice for Simulator-based Models with the Jensen--Shannon Divergence](https://mlsanity.com/paper/2206.04110/) (2022)

## Abstract (arXiv)

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.