Particle Markov chain Monte Carlo for efficient numerical simulation

Christophe Andrieu, Arnaud Doucet, Roman Holenstein

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

10 Citations (Scopus)

Abstract

Markov ChainMonte Carlo (MCMC) and sequentialMonte Carlo (SMC) methods are the two most popular classes of algorithms used to sample from general high-dimensional probability distributions. The theoretical convergence of MCMC algorithms is ensured under weak assumptions, but their practical performance is notoriously unsatisfactory when the proposal distributions used to explore the space are poorly chosen and/or if highly correlated variables are updated independently. We show here how it is possible to systematically design potentially very efficient high-dimensional proposal distributions for MCMC by using SMC techniques. We demonstrate how this novel approach allows us to design effective MCMC algorithms in complex scenarios. This is illustrated by a problem of Bayesian inference for a stochastic kinetic model.

Original languageEnglish
Title of host publicationMonte Carlo and Quasi-Monte Carlo Methods 2008
PublisherSpringer Verlag
Pages45-60
Number of pages16
ISBN (Print)9783642041068
DOIs
Publication statusPublished - 2009
Event8th International Conference on Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing, MCQMC 2008 - Montreal, QC, Canada
Duration: 6 Jul 200811 Jul 2008

Conference

Conference8th International Conference on Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing, MCQMC 2008
CountryCanada
CityMontreal, QC
Period6/07/0811/07/08

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    Andrieu, C., Doucet, A., & Holenstein, R. (2009). Particle Markov chain Monte Carlo for efficient numerical simulation. In Monte Carlo and Quasi-Monte Carlo Methods 2008 (pp. 45-60). Springer Verlag. https://doi.org/10.1007/978-3-642-04107-5-3