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Asymptotics of ABC

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Chapter (peer-reviewed) › peer-review

Published

Standard

Asymptotics of ABC. / Fearnhead, Paul.
Handbook of Approximate Bayesian Computation . ed. / Scott Sisson; Yanan Fan; Mark Beaumont. CRC Press, 2018. p. 269-288.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Chapter (peer-reviewed) › peer-review

Harvard

Fearnhead, P 2018, Asymptotics of ABC. in S Sisson, Y Fan & M Beaumont (eds), Handbook of Approximate Bayesian Computation . CRC Press, pp. 269-288. <https://www.crcpress.com/Handbook-of-Approximate-Bayesian-Computation/Sisson-Fan-Beaumont/p/book/9781439881507>

APA

Fearnhead, P. (2018). Asymptotics of ABC. In S. Sisson, Y. Fan, & M. Beaumont (Eds.), Handbook of Approximate Bayesian Computation (pp. 269-288). CRC Press. https://www.crcpress.com/Handbook-of-Approximate-Bayesian-Computation/Sisson-Fan-Beaumont/p/book/9781439881507

Vancouver

Fearnhead P. Asymptotics of ABC. In Sisson S, Fan Y, Beaumont M, editors, Handbook of Approximate Bayesian Computation . CRC Press. 2018. p. 269-288

Author

Fearnhead, Paul. / Asymptotics of ABC. Handbook of Approximate Bayesian Computation . editor / Scott Sisson ; Yanan Fan ; Mark Beaumont. CRC Press, 2018. pp. 269-288

Bibtex

@inbook{8a753ad8f3ba4c9286688e5265b5450d,
title = "Asymptotics of ABC",
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.",
keywords = "stat.ME, math.ST, stat.CO, stat.TH",
author = "Paul Fearnhead",
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",
year = "2018",
language = "English",
isbn = "9781439881507",
pages = "269--288",
editor = "Scott Sisson and Yanan Fan and Mark Beaumont",
booktitle = "Handbook of Approximate Bayesian Computation",
publisher = "CRC Press",

}

RIS

TY - CHAP

T1 - Asymptotics of ABC

AU - Fearnhead, Paul

N1 - 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

PY - 2018

Y1 - 2018

N2 - 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.

AB - 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.

KW - stat.ME

KW - math.ST

KW - stat.CO

KW - stat.TH

M3 - Chapter (peer-reviewed)

SN - 9781439881507

SP - 269

EP - 288

BT - Handbook of Approximate Bayesian Computation

A2 - Sisson, Scott

A2 - Fan, Yanan

A2 - Beaumont, Mark

PB - CRC Press

ER -