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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
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 | ||||||||
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| 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: |
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| Date of first compliant deposit: | 28 December 2015 | ||||||||
| 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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