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May 2010 The Random Walk Metropolis: Linking Theory and Practice Through a Case Study
Chris Sherlock, Paul Fearnhead, Gareth O. Roberts
Statist. Sci. 25(2): 172-190 (May 2010). DOI: 10.1214/10-STS327


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.


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Chris Sherlock. Paul Fearnhead. Gareth O. Roberts. "The Random Walk Metropolis: Linking Theory and Practice Through a Case Study." Statist. Sci. 25 (2) 172 - 190, May 2010.


Published: May 2010
First available in Project Euclid: 19 November 2010

zbMATH: 1328.60177
MathSciNet: MR2789988
Digital Object Identifier: 10.1214/10-STS327

Rights: Copyright © 2010 Institute of Mathematical Statistics


Vol.25 • No. 2 • May 2010
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