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Parallel and interacting Markov chain Monte Carlo algorithm

Fabien Campillo 1 Rivo Rakotozafy 2 Vivien Rossi 3 
1 MERE - Water Resource Modeling
CRISAM - Inria Sophia Antipolis - Méditerranée , INRA - Institut National de la Recherche Agronomique : UMR0729
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 examples that it possesses many advantages. This approach is applied to a biomass evolution model.
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https://hal.inria.fr/inria-00506127
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Submitted on : Tuesday, July 27, 2010 - 12:35:11 PM
Last modification on : Friday, February 4, 2022 - 3:19:23 AM

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Fabien Campillo, Rivo Rakotozafy, Vivien Rossi. Parallel and interacting Markov chain Monte Carlo algorithm. Mathematics and Computers in Simulation, Elsevier, 2009, 79 (12), pp.3424--3433. ⟨10.1016/j.matcom.2009.04.010⟩. ⟨inria-00506127⟩

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