@Article{Campillo_etal2009,
author="Campillo, F.
and Rakotozafy, R.
and Rossi, V.",
title="Parallel and interacting Markov chain Monte Carlo algorithm",
journal="Mathematics and Computers in Simulation",
year="2009",
publisher="ELSEVIER SCIENCE BV",
volume="79",
number="12",
pages="3424--3433",
optkeywords="Markov chain Monte Carlo method",
optkeywords="Interacting chains",
optkeywords="Hidden Markov model",
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. (C) 2009 IMACS. Published by Elsevier B.V. All rights reserved.",
optnote="ISI:000269289100006",
optnote="exported from refbase (http://php.ecofog.gf/refbase/show.php?record=197), last updated on Wed, 04 May 2011 10:48:04 -0300",
issn="0378-4754"
}