%0 Journal Article
%T Parallel and interacting Markov chain Monte Carlo algorithm
%A Campillo, F.
%A Rakotozafy, R.
%A Rossi, V.
%J Mathematics and Computers in Simulation
%D 2009
%V 79
%N 12
%I ELSEVIER SCIENCE BV
%@ 0378-4754
%F Campillo_etal2009
%O ISI:000269289100006
%O exported from refbase (http://php.ecofog.gf/refbase/show.php?record=197), last updated on Wed, 04 May 2011 10:48:04 -0300
%X 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. (C) 2009 IMACS. Published by Elsevier B.V. All rights reserved.
%K Markov chain Monte Carlo method
%K Interacting chains
%K Hidden Markov model
%P 3424-3433