TY  - JOUR
AU  - Campillo, F.
AU  - Rakotozafy, R.
AU  - Rossi, V.
PY  - 2009//
TI  - Parallel and interacting Markov chain Monte Carlo algorithm
T2  - Math. Comput. Simul.
JO  - Mathematics and Computers in Simulation
SP  - 3424
EP  - 3433
VL  - 79
IS  - 12
PB  - ELSEVIER SCIENCE BV
KW  - Markov chain Monte Carlo method
KW  - Interacting chains
KW  - Hidden Markov model
N2  - 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.
SN  - 0378-4754
N1  - ISI:000269289100006
ID  - Campillo_etal2009
ER  -