Parallel and interacting Markov chains Monte Carlo method
概率论
2007-05-23 v1
摘要
In many situations it is important to be able to propose independent realizations of a given distribution law. We propose a strategy for making parallel Monte Carlo Markov Chains (MCMC) interact in order to get an approximation of an independent -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 target measures. Compared to independent parallel chains this method is more time consuming, but we show through concrete examples that it possesses many advantages: it can speed up convergence toward the target law as well as handle the multi-modal case.
引用
@article{arxiv.math/0610181,
title = {Parallel and interacting Markov chains Monte Carlo method},
author = {Fabien Campillo and Vivien Rossi},
journal= {arXiv preprint arXiv:math/0610181},
year = {2007}
}