Asymptotic Properties of Approximate Bayesian Computation

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Approximate 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.

David T. Frazier
DECRA Fellow and Associate Professor in Econometrics and Statistics

My research interests include simulation-based statistical theory and inference, approximate Bayesian analysis, forecasting and scoring rules.