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Author
Campillo, F.; Rakotozafy, R.; Rossi, V.
Title
Parallel and interacting Markov chain Monte Carlo algorithm
Type
Journal Article
Year
2009
Publication
Mathematics and Computers in Simulation
Abbreviated Journal
Math. Comput. Simul.
Volume
79
Issue
12
Pages
3424-3433
Keywords
Markov chain Monte Carlo method; Interacting chains; 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.
Address
[Rossi, Vivien] CIRAD, Res Unit, Montpellier, France, Email: Fabien.Campillo@inria.fr
Corporate Author
Thesis
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ELSEVIER SCIENCE BV
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Summary Language
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0378-4754
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Notes
ISI:000269289100006
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no
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EcoFoG @ eric.marcon @
Serial
197
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