Parallel and interacting Markov chains Monte Carlo method
| dc.creator | Campillo, Fabien | |
| dc.creator | Rossi, Vivien | |
| dc.date | 2006-10-05 | |
| dc.date.accessioned | 2026-07-07T07:28:45Z | |
| dc.date.available | 2026-07-07T07:28:45Z | |
| dc.description | 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 concrete examples that it possesses many advantages: it can speed up convergence toward the target law as well as handle the multi-modal case. | |
| dc.identifier | https://arxiv.org/abs/math/0610181 | |
| dc.identifier | http://arxiv.org/abs/math/0610181 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/117847 | |
| dc.subject | Probability | |
| dc.title | Parallel and interacting Markov chains Monte Carlo method | |
| dc.type | text |