# Parallel and interacting Markov chains Monte Carlo method

1 ASPI - Applications of interacting particle systems to statistics
UR1 - Université de Rennes 1, Inria Rennes – Bretagne Atlantique , CNRS - Centre National de la Recherche Scientifique : UMR6074
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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https://hal.inria.fr/inria-00103871
Contributor : Fabien Campillo <>
Submitted on : Thursday, October 5, 2006 - 2:01:39 PM
Last modification on : Tuesday, February 2, 2021 - 2:38:01 PM
Long-term archiving on: : Tuesday, April 6, 2010 - 6:29:41 PM

### Citation

Fabien Campillo, Vivien Rossi. Parallel and interacting Markov chains Monte Carlo method. [Research Report] 2006. ⟨inria-00103871v1⟩

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