Convergence of regression-adjusted approximate Bayesian computation

Li, Wentao and Fearnhead, Paul (2018) Convergence of regression-adjusted approximate Bayesian computation. Biometrika, 105 (2). pp. 301-318. ISSN 0006-3444

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Abstract

We present asymptotic results for the regression-adjusted version of approximate Bayesian computation introduced by Beaumont et al. (2002). We show that for an appropriate choice of the bandwidth, regression adjustment will lead to a posterior that, asymptotically, correctly quantifies uncertainty. Furthermore, for such a choice of bandwidth we can implement an importance sampling algorithm to sample from the posterior whose acceptance probability tends to unity as the data sample size increases. This compares favourably to results for standard approximate Bayesian computation, where the only way to obtain a posterior that correctly quantifies uncertainty is to choose a much smaller bandwidth; one for which the acceptance probability tends to zero and hence for which Monte Carlo error will dominate.

Item Type:
Journal Article
Journal or Publication Title:
Biometrika
Additional Information:
This is a pre-copy-editing, author-produced PDF of an article accepted for publication in Biometrika following peer review. The definitive publisher-authenticated version Wentao Li, Paul Fearnhead; Convergence of regression-adjusted approximate Bayesian computation, Biometrika, Volume 105, Issue 2, 1 June 2018, Pages 301–318, https://doi.org/10.1093/biomet/asx081 is available online at: https://academic.oup.com/biomet/article/105/2/301/4827648
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1100/1101
Subjects:
ID Code:
82050
Deposited By:
Deposited On:
07 Oct 2016 10:40
Refereed?:
Yes
Published?:
Published
Last Modified:
29 Mar 2020 04:51