Regeneration in Markov Chain Samplers
作者:
Per Mykland,
Luke Tierney,
Bin Yu,
期刊:
Journal of the American Statistical Association
(Taylor Available online 1995)
卷期:
Volume 90,
issue 429
页码: 233-241
ISSN:0162-1459
年代: 1995
DOI:10.1080/01621459.1995.10476507
出版商: Taylor & Francis Group
关键词: Gibbs sampling;Hybrid sampler;Markov chain Monte Carlo;Metropolis algorithm;Simulation output analysis;Split chain
数据来源: Taylor
摘要:
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.
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