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Parallel and interacting Markov chains Monte Carlo method

Abstract : In many situations it is important to be able to propose $N$ independent realizations of a given distribution law. We propose a strategy for making $N$ parallel Monte Carlo Markov Chains (MCMC) interact in order to get an approximation of an independent $N$-sample of a given target law. In this method each individual chain proposes candidates for all other chains. We prove that the set of interacting chains is itself a MCMC method for the product of $N$ target measures. Compared to independent parallel chains this method is more time consuming, but we show through concrete examples that it possesses many advantages: it can speed up convergence toward the target law as well as handle the multi-modal case.
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Contributor : Rapport de Recherche Inria <>
Submitted on : Thursday, November 2, 2006 - 12:01:06 PM
Last modification on : Tuesday, February 2, 2021 - 2:38:01 PM
Long-term archiving on: : Monday, September 20, 2010 - 4:42:20 PM



Fabien Campillo, Vivien Rossi. Parallel and interacting Markov chains Monte Carlo method. [Research Report] RR-6008, INRIA. 2006. ⟨inria-00103871v2⟩



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