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The random walk metropolis : linking theory and practice through a case study
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Sherlock, Chris, Fearnhead, Paul and Roberts, Gareth O. (2010) The random walk metropolis : linking theory and practice through a case study. Statistical Science, Vol.25 (No.2). pp. 172-190. doi:10.1214/10-STS327 ISSN 0883-4237.
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Official URL: http://dx.doi.org/10.1214/10-STS327
Abstract
The random walk Metropolis (RWM) is one of the most common Markov chain Monte Carlo algorithms in practical use today. Its theoretical properties have been extensively explored for certain classes of target, and a number of results with important practical implications have been derived. This article draws together a selection of new and existing key results and concepts and describes their implications. The impact of each new idea on algorithm efficiency is demonstrated for the practical example of the Markov modulated Poisson process (MMPP). A reparameterization of the MMPP which leads to a highly efficient RWM-within-Gibbs algorithm in certain circumstances is also presented.
Item Type: | Journal Article | ||||
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Subjects: | Q Science > QA Mathematics | ||||
Divisions: | Faculty of Science, Engineering and Medicine > Science > Statistics | ||||
Journal or Publication Title: | Statistical Science | ||||
Publisher: | American Institute of Mathematical Statistics | ||||
ISSN: | 0883-4237 | ||||
Official Date: | May 2010 | ||||
Dates: |
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Volume: | Vol.25 | ||||
Number: | No.2 | ||||
Number of Pages: | 19 | ||||
Page Range: | pp. 172-190 | ||||
DOI: | 10.1214/10-STS327 | ||||
Status: | Peer Reviewed | ||||
Publication Status: | Published | ||||
Access rights to Published version: | Restricted or Subscription Access |
Data sourced from Thomson Reuters' Web of Knowledge
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