Bayesian numerical inference for hidden Markov models

Fabien Campillo 1 Rivo Rakotozafy 2 Vivien Rossi 3
1 MERE - Water Resource Modeling
CRISAM - Inria Sophia Antipolis - Méditerranée , INRA - Institut National de la Recherche Agronomique : UMR0729
Abstract : 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.
Type de document :
Communication dans un congrès
International Conference on Applied Statistics for Development in Africa Sada'07, Feb 2007, Cotonou, Benin. 6 p., 2007
Liste complète des métadonnées

Littérature citée [6 références]  Voir  Masquer  Télécharger

https://hal.inria.fr/inria-00506398
Contributeur : Fabien Campillo <>
Soumis le : mardi 27 juillet 2010 - 15:48:10
Dernière modification le : mercredi 10 octobre 2018 - 14:28:15
Document(s) archivé(s) le : jeudi 28 octobre 2010 - 17:32:35

Fichier

campillo2007b.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

  • HAL Id : inria-00506398, version 1

Collections

Citation

Fabien Campillo, Rivo Rakotozafy, Vivien Rossi. Bayesian numerical inference for hidden Markov models. International Conference on Applied Statistics for Development in Africa Sada'07, Feb 2007, Cotonou, Benin. 6 p., 2007. 〈inria-00506398〉

Partager

Métriques

Consultations de la notice

578

Téléchargements de fichiers

175