Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
1141009 | Mathematics and Computers in Simulation | 2009 | 10 Pages |
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.
Related Topics
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Engineering
Control and Systems Engineering
Authors
Fabien Campillo, Rivo Rakotozafy, Vivien Rossi,