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dc.contributor.authorCappé, Olivier
HAL ID: 1534
ORCID: 0000-0001-7415-8669
dc.contributor.authorGuillin, Arnaud
HAL ID: 5334
dc.contributor.authorMarin, Jean-Michel
HAL ID: 9121
ORCID: 0000-0001-7451-9719
dc.contributor.authorRobert, Christian P.
dc.date.accessioned2011-04-27T14:28:23Z
dc.date.available2011-04-27T14:28:23Z
dc.date.issued2004
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/6072
dc.language.isoenen
dc.subjectMultiple scalesen
dc.subjectIon channel modelen
dc.subjectimportance samplingen
dc.subjecthidden semi-Markov modelen
dc.subjectadaptive algorithmsen
dc.subject.ddc519en
dc.subject.classificationjelC11en
dc.titlePopulation Monte Carloen
dc.typeArticle accepté pour publication ou publié
dc.description.abstractenImportance sampling methods can be iterated like MCMC algorithms, while being more robust against dependence and starting values. The population Monte Carlo principle consists of iterated generations of importance samples, with importance functions depending on the previously generated importance samples. The advantage over MCMC algorithms is that the scheme is unbiased at any iteration and can thus be stopped at any time, while iterations improve the performances of the importance function, thus leading to an adaptive importance sampling. We illustrate this method on a mixture example with multiscale importance functions. A second example reanalyzes the ion channel model using an importance sampling scheme based on a hidden Markov representation, and compares population Monte Carlo with a corresponding MCMC algorithm.en
dc.relation.isversionofjnlnameJournal of Computational and Graphical Statistics
dc.relation.isversionofjnlvol13en
dc.relation.isversionofjnlissue4en
dc.relation.isversionofjnldate2004
dc.relation.isversionofjnlpages907-929en
dc.relation.isversionofdoihttp://dx.doi.org/10.1198/106186004X12803en
dc.description.sponsorshipprivateouien
dc.relation.isversionofjnlpublisherAmerican Statistical Associationen
dc.subject.ddclabelProbabilités et mathématiques appliquéesen


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