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Article Dans Une Revue Mathematics and Computers in Simulation Année : 2009

Parallel and interacting Markov chain Monte Carlo algorithm

Fabien Campillo
Vivien Rossi

Résumé

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.

Dates et versions

inria-00506127 , version 1 (27-07-2010)

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