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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.
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Submitted on : Tuesday, July 27, 2010 - 3:48:10 PM
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  • HAL Id : inria-00506398, version 1
  • PRODINRA : 40042


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. ⟨inria-00506398⟩



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