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

In: Proceedings of COMPSTAT'2010

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  • Michael G.B. Blum

    (CNRS, UJF Grenoble, Laboratoire TIMC-IMAG, Faculté de Médecine)

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.

Suggested Citation

  • Michael G.B. Blum, 2010. "Choosing the Summary Statistics and the Acceptance Rate in Approximate Bayesian Computation," Springer Books, in: Yves Lechevallier & Gilbert Saporta (ed.), Proceedings of COMPSTAT'2010, pages 47-56, Springer.
    • Handle: RePEc:spr:sprchp:978-3-7908-2604-3_4
    DOI: 10.1007/978-3-7908-2604-3_4
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