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The random walk Metropolis : linking theory and practice through a case study.

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The random walk Metropolis : linking theory and practice through a case study. / Sherlock, Chris; Fearnhead, Paul; Roberts, Gareth.
In: Statistical Science, Vol. 25, No. 2, 05.2010, p. 172-190.

Research output: Contribution to Journal/Magazine › Journal article

Harvard

Sherlock, C, Fearnhead, P & Roberts, G 2010, 'The random walk Metropolis : linking theory and practice through a case study.', Statistical Science, vol. 25, no. 2, pp. 172-190. https://doi.org/10.1214/10-STS327

APA

Sherlock, C., Fearnhead, P., & Roberts, G. (2010). The random walk Metropolis : linking theory and practice through a case study. Statistical Science, 25(2), 172-190. https://doi.org/10.1214/10-STS327

Vancouver

Sherlock C, Fearnhead P, Roberts G. The random walk Metropolis : linking theory and practice through a case study. Statistical Science. 2010 May;25(2):172-190. doi: 10.1214/10-STS327

Author

Sherlock, Chris ; Fearnhead, Paul ; Roberts, Gareth. / The random walk Metropolis : linking theory and practice through a case study. In: Statistical Science. 2010 ; Vol. 25, No. 2. pp. 172-190.

Bibtex

@article{e109dc5e70d54eb99522f91e662c4bb3,
title = "The random walk Metropolis : linking theory and practice through a case study.",
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 reparameterisation of the MMPP which leads to a highly efficient RWM within Gibbs algorithm in certain circumstances is also developed.",
keywords = "Random walk Metropolis, Metropolis-Hastings, MCMC, adaptive MCMC, MMPP",
author = "Chris Sherlock and Paul Fearnhead and Gareth Roberts",
year = "2010",
month = may,
doi = "10.1214/10-STS327",
language = "English",
volume = "25",
pages = "172--190",
journal = "Statistical Science",
issn = "0883-4237",
publisher = "Institute of Mathematical Statistics",
number = "2",

}

RIS

TY - JOUR

T1 - The random walk Metropolis : linking theory and practice through a case study.

AU - Sherlock, Chris

AU - Fearnhead, Paul

AU - Roberts, Gareth

PY - 2010/5

Y1 - 2010/5

N2 - 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 reparameterisation of the MMPP which leads to a highly efficient RWM within Gibbs algorithm in certain circumstances is also developed.

AB - 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 reparameterisation of the MMPP which leads to a highly efficient RWM within Gibbs algorithm in certain circumstances is also developed.

KW - Random walk Metropolis

KW - Metropolis-Hastings

KW - MCMC

KW - adaptive MCMC

KW - MMPP

U2 - 10.1214/10-STS327

DO - 10.1214/10-STS327

M3 - Journal article

VL - 25

SP - 172

EP - 190

JO - Statistical Science

JF - Statistical Science

SN - 0883-4237

IS - 2

ER -