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Asymptotics of ABC

Fearnhead, Paul (2018) Asymptotics of ABC. In: Handbook of Approximate Bayesian Computation. CRC Press. ISBN 9781439881507 (In Press)

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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.

Item Type: Contribution in Book/Report/Proceedings
Additional Information: 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
Uncontrolled Keywords: stat.ME ; math.ST ; stat.CO ; stat.TH
Subjects:
Departments: Faculty of Science and Technology > Mathematics and Statistics
ID Code: 86892
Deposited By: ep_importer_pure
Deposited On: 30 Jun 2017 10:42
Refereed?: Yes
Published?: In Press
Last Modified: 07 Jul 2017 10:12
Identification Number:
URI: http://eprints.lancs.ac.uk/id/eprint/86892

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