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

Michael G.B. Blum ()
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Michael G.B. Blum: CNRS, UJF Grenoble, Laboratoire TIMC-IMAG, Faculté de Médecine

A chapter in Proceedings of COMPSTAT'2010, 2010, pp 47-56 from Springer

Abstract: 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.

Keywords: approximate Bayesian computation; evidence approximation; empirical Bayes; Bayesian local regression (search for similar items in EconPapers)
Date: 2010
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-7908-2604-3_4

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DOI: 10.1007/978-3-7908-2604-3_4

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