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Asymptotics of ABC

Research output: Contribution in Book/Report/ProceedingsChapter (peer-reviewed)

Forthcoming
Publication date31/01/2018
Host publicationHandbook of Approximate Bayesian Computation
EditorsScott Sisson, Yanan Fan, Mark Beaumont
PublisherCRC Press
ISBN (Print)9781439881507
<mark>Original language</mark>English

Abstract

We present an informal review of recent work on the asymptotics of Approximate Bayesian Computation (ABC). In particular we focus on how does the ABC posterior, or point estimates obtained by ABC, behave in the limit as we have more data? The results we review show that ABC can perform well in terms of point estimation, but standard implementations will over-estimate the uncertainty about the parameters. If we use the regression correction of Beaumont et al. then ABC can also accurately quantify this uncertainty. The theoretical results also have practical implications for how to implement ABC.

Bibliographic note

This document is due to appear as a chapter of the forthcoming Handbook of Approximate Bayesian Computation (ABC) edited by S. Sisson, Y. Fan, and M. Beaumont