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Variance bounding and geometric ergodicity of Markov chain Monte Carlo kernels for approximate Bayesian computation

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Lee, Anthony and Łatuszyński, Krzysztof (2014) Variance bounding and geometric ergodicity of Markov chain Monte Carlo kernels for approximate Bayesian computation. Biometrika, Volume 101 (Number 3). pp. 655-671. doi:10.1093/biomet/asu027

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Official URL: http://dx.doi.org/10.1093/biomet/asu027

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Abstract

Approximate Bayesian computation has emerged as a standard computational tool when dealing with intractable likelihood functions in Bayesian inference. We show that many common Markov chain Monte Carlo kernels used to facilitate inference in this setting can fail to be variance bounding and hence geometrically ergodic, which can have consequences for the reliability of estimates in practice. This phenomenon is typically independent of the choice of tolerance in the approximation. We prove that a recently introduced Markov kernel can inherit the properties of variance bounding and geometric ergodicity from its intractable Metropolis–Hastings counterpart, under reasonably weak conditions. We show that the computational cost of this alternative kernel is bounded whenever the prior is proper, and present indicative results for an example where spectral gaps and asymptotic variances can be computed, as well as an example involving inference for a partially and discretely observed, time-homogeneous, pure jump Markov process. We also supply two general theorems, one providing a simple sufficient condition for lack of variance bounding for reversible kernels and the other providing a positive result concerning inheritance of variance bounding and geometric ergodicity for mixtures of reversible kernels.

Item Type: Journal Article
Subjects: Q Science > QA Mathematics
Divisions: Faculty of Science > Statistics
Library of Congress Subject Headings (LCSH): Bayesian statistical decision theory, Markov processes, Monte Carlo method, Ergodic theory
Journal or Publication Title: Biometrika
Publisher: Biometrika Trust
ISSN: 0006-3444
Official Date: September 2014
Dates:
DateEvent
September 2014Published
5 August 2014Available
October 2012Submitted
Volume: Volume 101
Number: Number 3
Page Range: pp. 655-671
DOI: 10.1093/biomet/asu027
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Open Access
Funder: University of Warwick. Centre for Research in Statistical Methodology, Engineering and Physical Sciences Research Council (EPSRC)

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