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<identifier>oai:HAL:inria-00506398v1</identifier>
<datestamp>2018-01-11</datestamp>
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<publisher>HAL CCSD</publisher>
<title lang=da>Bayesian numerical inference for hidden Markov models</title>
<creator>Campillo, Fabien</creator>
<creator>Rakotozafy, Rivo</creator>
<creator>Rossi, Vivien</creator>
<contributor>Water Resource Modeling (MERE) ; Inria Sophia Antipolis - Méditerranée (CRISAM) ; Institut National de Recherche en Informatique et en Automatique (Inria) - Institut National de Recherche en Informatique et en Automatique (Inria) - Institut National de la Recherche Agronomique (INRA)</contributor>
<contributor>Université de Fianarantsoa [Fianarantsoa]</contributor>
<contributor>Ecologie des forêts de Guyane (ECOFOG) ; Centre de Coopération Internationale en Recherche Agronomique pour le Développement (CIRAD) - Institut National de la Recherche Agronomique (INRA) - Université des Antilles et de la Guyane (UAG) - AgroParisTech - Centre National de la Recherche Scientifique (CNRS)</contributor>
<description>International audience</description>
<source>International Conference on Applied Statistics for Development in Africa Sada'07</source>
<coverage>Cotonou, Benin</coverage>
<identifier>inria-00506398</identifier>
<identifier>https://hal.inria.fr/inria-00506398</identifier>
<identifier>https://hal.inria.fr/inria-00506398/document</identifier>
<identifier>https://hal.inria.fr/inria-00506398/file/campillo2007b.pdf</identifier>
<source>https://hal.inria.fr/inria-00506398</source>
<source>International Conference on Applied Statistics for Development in Africa Sada'07, Feb 2007, Cotonou, Benin. 6 p., 2007</source>
<language>en</language>
<subject lang=en>MARKOV CHAIN MONTE CARLO METHOD, INTERACTING CHAINS, HIDDEN MARKOV MODEL, LOI DE DISTRIBUTION</subject>
<subject>[MATH.MATH-PR] Mathematics [math]/Probability [math.PR]</subject>
<type>info:eu-repo/semantics/conferenceObject</type>
<type>Conference papers</type>
<description lang=en>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.</description>
<date>2007-02-27</date>
</dc>
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