Parallel and interacting Markov chain Monte Carlo algorithm

被引：9
|
作者：
Campillo, Fabien [2 ]
Rakotozafy, Rivo [3 ]
Rossi, Vivien [1 ]
机构：
[1] CIRAD, Res Unit, Montpellier, France
[2] INRIA, INRA, MERE Project Team, Montpellier, France
[3] Univ Fianarantsoa, Fianarantsoa, Madagascar
来源：
MATHEMATICS AND COMPUTERS IN SIMULATION | 2009年 / 79卷 / 12期
关键词：
Markov chain Monte Carlo method; Interacting chains; Hidden Markov model; CONVERGENCE;
D O I：
10.1016/j.matcom.2009.04.010
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
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.
引用
收藏
页码：3424 / 3433
页数：10
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