Asymptotics of ABC

Fearnhead, Paul (2018) Asymptotics of ABC. In: Handbook of Approximate Bayesian Computation. CRC Press, pp. 269-288. ISBN 9781439881507

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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.

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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
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Deposited On:
30 Jun 2017 09:42
Last Modified:
12 Oct 2023 10:05