Asymptotic properties of approximate Bayesian computation

D T Frazier, G M Martin, C P Robert and J Rousseau

Biometrika, 2018, vol. 105, issue 3, 593-607

Abstract: SummaryApproximate Bayesian computation allows for statistical analysis using models with intractable likelihoods. In this paper we consider the asymptotic behaviour of the posterior distribution obtained by this method. We give general results on the rate at which the posterior distribution concentrates on sets containing the true parameter, the limiting shape of the posterior distribution, and the asymptotic distribution of the posterior mean. These results hold under given rates for the tolerance used within the method, mild regularity conditions on the summary statistics, and a condition linked to identification of the true parameters. Implications for practitioners are discussed.

Keywords: Approximate Bayesian computation; Asymptotics; Bernstein–von Mises theorem; Likelihood-free method; Posterior concentration (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (15)

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