Éditeur(s) : HAL CCSD Elsevier
Résumé : International audience
ISSN: 0378-4754

inria-00506127
https://hal.inria.fr/inria-00506127
DOI : 10.1016/j.matcom.2009.04.010

Éditeur(s) : HAL CCSD
Résumé : International audience
Joint Meeting of the Statistical Society of Canada and the Société Française de Statistique
Ottawa, Canada

hal-00652079
https://hal.inria.fr/hal-00652079

Éditeur(s) : HAL CCSD
Résumé : International audience
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.
International Conference on Applied Statistics for Development in Africa Sada'07
Cotonou, Benin

inria-00506398
https://hal.inria.fr/inria-00506398
https://hal.inria.fr/inria-00506398/document
https://hal.inria.fr/inria-00506398/file/campillo2007b.pdf

Éditeur(s) : HAL CCSD
Résumé : National audience
Journées MAS de la SMAI
Rennes, France

hal-00652081
https://hal.inria.fr/hal-00652081

Éditeur(s) : HAL CCSD
Résumé : International audience
Bayesian modelling is fluently employed to assess natural ressources. It is associated with Monte Carlo Markov Chains (MCMC) to get an approximation of the distribution law of interest. Hence in such situations it is important to be able to propose N independent realiza- tions of this distribution law. We propose a strategy for making N parallel Monte Carlo Markov Chains 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 example that it possesses many advantages. This approach will be applied to a forest dynamic model.
International Conference on Approximation Methods and Numerical Modelling in Environment and Natural Resources
Granada, Spain

inria-00506591
https://hal.inria.fr/inria-00506591
https://hal.inria.fr/inria-00506591/document
https://hal.inria.fr/inria-00506591/file/campillo2007c.pdf

Éditeur(s) : HAL CCSD Institute of Electrical and Electronics Engineers
Résumé : International audience
The state-space modeling of partially observed dynamical systems generally requires estimates of unknown parameters. The dynamic state vector together with the static parameter vector can be considered as an augmented state vector. Classical filtering methods, such as the extended Kalman filter (EKF) and the bootstrap particle filter (PF), fail to estimate the augmented state vector. For these classical filters to handle the augmented state vector, a dynamic noise term should be artificially added to the parameter components or to the deterministic component of the dynamical system. However, this approach degrades the estimation performance of the filters. We propose a variant of the PF based on convolution kernel approximation techniques. This approach is tested on a simulated case study.
ISSN: 0018-9251

inria-00459886
https://hal.inria.fr/inria-00459886
DOI : 10.1109/TAES.2009.5259183

Éditeur(s) : HAL CCSD INRIA
Résumé : International audience
Computational probabilistic modeling and Bayesian inference has met a great success over the past fifteen years through the development of Monte Carlo methods and the ever increasing performance of computers. Through methods such as Monte Carlo Markov chain and sequential Monte Carlo Bayesian inference effectively combines with Markovian modelling. This approach has been very successful in ecology and agronomy. We analyze the development of this approach applied to a few examples of natural resources management.
La modélisation probabiliste et l'inférence bayésienne computationnelles rencontrent un très grand succès depuis une quinzaine d'années grâce au développement des méthodes de Monte Carlo et aux performances toujours croissantes des moyens de calcul. Au travers d'outils comme les méthodes de Monte Carlo par chaîne de Markov et les méthodes de Monte Carlo séquentielles, l'inférence bayésienne se combine efficacement à la modélisation markovienne. Cette approche est également très répendue dans le domaine de l'écologie et l'agronomie. Nous faisons le point sur les développements de cette approche appliquée à quelques exemples de gestion de ressources naturelles.
ARIMA

inria-00506383
https://hal.inria.fr/inria-00506383
https://hal.inria.fr/inria-00506383/document

Éditeur(s) : HAL CCSD INRIA
Résumé : International audience
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.
Les méthodes de Monte Carlo par chaînes de Markov (MCMC) couplées à des modèles de Markov cachés sont utilisées dans de nombreux domaines, notamment en environnement et en écologie. Sur des exemples simples, nous montrons que la vitesse de convergence de ces méthodes peut être très faible. Nous proposons de mettre en interaction plusieurs algorithmes MCMC pour accélérer cette convergence. Nous appliquons ces méthodes à un modèle d'évolution de la biomasse d'une pêcherie.
ISSN: 1638-5713

inria-00506386
https://hal.inria.fr/inria-00506386
https://hal.inria.fr/inria-00506386/document
https://hal.inria.fr/inria-00506386/file/campillo2008d.pdf