PT Journal
AU Campillo, F
   Rakotozafy, R
   Rossi, V
TI Parallel and interacting Markov chain Monte Carlo algorithm
SO Mathematics and Computers in Simulation
JI Math. Comput. Simul.
PY 2009
BP 3424
EP 3433
VL 79
IS 12
DE Markov chain Monte Carlo method; Interacting chains; Hidden Markov model
AB 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.
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