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Adaptive Gibbs samplers and related MCMC methods
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Łatuszyński, Krzysztof, Roberts, Gareth O. and Rosenthal, Jeffrey S. (Jeffrey Seth) (2013) Adaptive Gibbs samplers and related MCMC methods. Annals of Probability, 23 (1). pp. 66-98. doi:10.1214/11-AAP806 ISSN 0091-1798.
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Official URL: http://dx.doi.org/10.1214/11-AAP806
Abstract
We consider various versions of adaptive Gibbs and Metropolis- within-Gibbs samplers, which update their selection probabilities (and perhaps also their proposal distributions) on the y during a run, by learning
as they go in an attempt to optimise the algorithm.We present a cautionary example of how even a simple-seeming adaptive Gibbs sampler may fail to
converge.We then present various positive results guaranteeing convergence of adaptive Gibbs samplers under certain conditions.
Item Type: | Journal Article | ||||
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Subjects: | Q Science > QA Mathematics | ||||
Divisions: | Faculty of Science, Engineering and Medicine > Science > Statistics | ||||
Library of Congress Subject Headings (LCSH): | Monte Carlo method, Markov processes, Sampling (Statistics) | ||||
Journal or Publication Title: | Annals of Probability | ||||
Publisher: | Institute of Mathematical Statistics | ||||
ISSN: | 0091-1798 | ||||
Official Date: | 2013 | ||||
Dates: |
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Volume: | 23 | ||||
Number: | 1 | ||||
Page Range: | pp. 66-98 | ||||
DOI: | 10.1214/11-AAP806 | ||||
Status: | Peer Reviewed | ||||
Publication Status: | Published | ||||
Access rights to Published version: | Restricted or Subscription Access |
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