Parallel and interacting Markov chain Monte Carlo algorithm

Campillo Fabien, Rakotozafy Rivo, Rossi Vivien. 2009. Parallel and interacting Markov chain Monte Carlo algorithm. Mathematics and Computers in Simulation, 79 (12) : 3424-3433.

Quartile : Q2, Sujet : MATHEMATICS, APPLIED / Quartile : Q3, Sujet : COMPUTER SCIENCE, SOFTWARE ENGINEERING / Quartile : Q3, Sujet : COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Résumé : 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.

Mots-clés Agrovoc : modèle mathématique, biomasse

Classification Agris : U10 - Informatique, mathématiques et statistiques

Champ stratégique Cirad : Axe 1 (2005-2013) - Intensification écologique

Auteurs et affiliations

• Campillo Fabien, INRA (FRA)
• Rakotozafy Rivo, Université de Fianarantsoa (MDG)
• Rossi Vivien, CIRAD-ES-UPR Dynamique forestière (FRA) ORCID: 0000-0001-5458-1523

Source : Cirad - Agritrop (https://agritrop.cirad.fr/550173/)

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