Parallel and interacting Markov chain Monte Carlo algorithm |
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Authors: | Fabien Campillo Rivo Rakotozafy Vivien Rossi |
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Affiliation: | aINRIA/INRA, MERE Project-Team, Montpellier, France;bUniversity of Fianarantsoa, Fianarantsoa, Madagascar;cCIRAD, Research Unit, Dynamics of Natural Forests, Montpellier, France |
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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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Keywords: | Markov chain Monte Carlo method Interacting chains Hidden Markov model |
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