Éditeur(s) :
HAL CCSD Résumé : International audience
In many situations it is important to be able to propose N independent real- izations 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 indepen- dent N-sample of a given target law. In this method each individual chain proposes can- didates 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.
International Conference on Applied Statistics for Development in Africa Sada'07
Cotonou, Benin
inria-00506398
https://hal.inria.fr/inria-00506398 https://hal.inria.fr/inria-00506398/document https://hal.inria.fr/inria-00506398/file/campillo2007b.pdf