Choosing the Summary Statistics and the Acceptance Rate in Approximate Bayesian Computation

Abstract

Approximate Bayesian Computation encompasses a family of likelihoodfree algorithms for performing Bayesian inference in models defined in terms of a generating mechanism. The different algorithms rely on simulations of some summary statistics under the generative model and a rejection criterion that determines if a simulation is rejected or not. In this paper, I incorporate Approximate Bayesian Computation into a local Bayesian regression framework. Using an empirical Bayes approach, we provide a simple criterion for 1) choosing the threshold above which a simulation should be rejected, 2) choosing the subset of informative summary statistics, and 3) choosing if a summary statistic should be log-transformed or not.