Skip to Main content Skip to Navigation
Journal articles

Méthodes MCMC en interaction pour l'évaluation de ressources naturelles

Fabien Campillo 1 Philippe Cantet 2 Rivo Rakotozafy 3 Vivien Rossi 4 
1 MERE - Water Resource Modeling
CRISAM - Inria Sophia Antipolis - Méditerranée , INRA - Institut National de la Recherche Agronomique : UMR0729
Abstract : Markov chain Monte Carlo (MCMC) methods together with hidden Markov models are extensively used in the Bayesian inference for many scientific fields like environment and ecology. Through simulated examples we show that the speed of convergence of these methods can be very low. In order to improve the convergence properties, we propose a method to make parallel chains interact. We apply this method to a biomass evolution model for fisheries.
Document type :
Journal articles
Complete list of metadata

Cited literature [41 references]  Display  Hide  Download

https://hal.inria.fr/inria-00506386
Contributor : Fabien Campillo Connect in order to contact the contributor
Submitted on : Tuesday, July 27, 2010 - 3:39:19 PM
Last modification on : Friday, August 5, 2022 - 2:38:11 PM
Long-term archiving on: : Thursday, October 28, 2010 - 5:31:55 PM

File

campillo2008d.pdf
Files produced by the author(s)

Identifiers

Citation

Fabien Campillo, Philippe Cantet, Rivo Rakotozafy, Vivien Rossi. Méthodes MCMC en interaction pour l'évaluation de ressources naturelles. Revue Africaine de la Recherche en Informatique et Mathématiques Appliquées, INRIA, 2008, Volume 8, Special Issue CARI'06, 2008 (n° spécial CARI’06), pp.64-80. ⟨10.46298/arima.1887⟩. ⟨inria-00506386⟩

Share

Metrics

Record views

304

Files downloads

751