untitled
<OAI-PMH schemaLocation=http://www.openarchives.org/OAI/2.0/ http://www.openarchives.org/OAI/2.0/OAI-PMH.xsd>
<responseDate>2018-01-15T15:39:07Z</responseDate>
<request identifier=oai:HAL:inria-00506591v1 verb=GetRecord metadataPrefix=oai_dc>http://api.archives-ouvertes.fr/oai/hal/</request>
<GetRecord>
<record>
<header>
<identifier>oai:HAL:inria-00506591v1</identifier>
<datestamp>2018-01-11</datestamp>
<setSpec>type:COMM</setSpec>
<setSpec>subject:math</setSpec>
<setSpec>collection:CIRAD</setSpec>
<setSpec>collection:AGROPARISTECH</setSpec>
<setSpec>collection:CNRS</setSpec>
<setSpec>collection:INRIA</setSpec>
<setSpec>collection:INRIA-SOPHIA</setSpec>
<setSpec>collection:INSMI</setSpec>
<setSpec>collection:INRIASO</setSpec>
<setSpec>collection:MERE</setSpec>
<setSpec>collection:UNIV-AG</setSpec>
<setSpec>collection:ECOFOG</setSpec>
<setSpec>collection:INRA</setSpec>
<setSpec>collection:INRIA_TEST</setSpec>
<setSpec>collection:AGREENIUM</setSpec>
<setSpec>collection:UCA-TEST</setSpec>
<setSpec>collection:UNIV-COTEDAZUR</setSpec>
</header>
<metadata><dc>
<publisher>HAL CCSD</publisher>
<title lang=en>Bayesian numerical inference for Markovian models -- Application to tropical forest dynamics</title>
<creator>Campillo, Fabien</creator>
<creator>Rakotozafy, R.</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 Approximation Methods and Numerical Modelling in Environment and Natural Resources</source>
<coverage>Granada, Spain</coverage>
<identifier>inria-00506591</identifier>
<identifier>https://hal.inria.fr/inria-00506591</identifier>
<identifier>https://hal.inria.fr/inria-00506591/document</identifier>
<identifier>https://hal.inria.fr/inria-00506591/file/campillo2007c.pdf</identifier>
<source>https://hal.inria.fr/inria-00506591</source>
<source>International Conference on Approximation Methods and Numerical Modelling in Environment and Natural Resources, Jul 2007, Granada, Spain. pp.53-56, 2007, International Conference on Approximation Methods and Numerical Modelling in Environment and Natural Resources, MAMERN'07</source>
<language>en</language>
<subject>[MATH.MATH-PR] Mathematics [math]/Probability [math.PR]</subject>
<type>info:eu-repo/semantics/conferenceObject</type>
<type>Conference papers</type>
<description lang=en>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.</description>
<date>2007-07-11</date>
</dc>
</metadata>
</record>
</GetRecord>
</OAI-PMH>
