Regeneration in Markov Chain Samplers

Per Mykland; Luke Tierney; Bin Yu

doi:10.1080/01621459.1995.10476507

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Journal article

Regeneration in Markov Chain Samplers

Per Mykland, Luke Tierney and Bin Yu

Journal of the American Statistical Association, Vol.90(429), pp.233-241

03/01/1995

DOI: 10.1080/01621459.1995.10476507

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Abstract

Markov chain sampling has recently received considerable attention, in particular in the context of Bayesian computation and maximum likelihood estimation. This article discusses the use of Markov chain splitting, originally developed for the theoretical analysis of general state-space Markov chains, to introduce regeneration into Markov chain samplers. This allows the use of regenerative methods for analyzing the output of these samplers and can provide a useful diagnostic of sampler performance. The approach is applied to several samplers, including certain Metropolis samplers that can be used on their own or in hybrid samplers, and is illustrated in several examples.

Gibbs sampling

Hybrid sampler

Markov chain Monte Carlo

Metropolis algorithm

Simulation output analysis

Split chain

Details

Title: Subtitle: Regeneration in Markov Chain Samplers
Creators: Per Mykland - University of Chicago
Luke Tierney - University of Minnesota
Bin Yu - University of California, Berkeley
Resource Type: Journal article
Publication Details: Journal of the American Statistical Association, Vol.90(429), pp.233-241
DOI: 10.1080/01621459.1995.10476507
ISSN: 0162-1459
eISSN: 1537-274X
Publisher: Taylor & Francis Group
Language: English
Date published: 03/01/1995
Academic Unit: Statistics and Actuarial Science
Record Identifier: 9984257740602771

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