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dc.contributor.authorRéveillac, Anthony
dc.date.accessioned2011-10-04T14:57:56Z
dc.date.available2011-10-04T14:57:56Z
dc.date.issued2013
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/7108
dc.language.isoenen
dc.subjectextended De Bruijn identitiesen
dc.subjectmutual informationen
dc.subjectMalliavin calculusen
dc.subjectBayesian estimationen
dc.subjectPoisson processen
dc.subject.ddc519en
dc.titleLikelihood ratios and inference for Poisson channelsen
dc.typeArticle accepté pour publication ou publié
dc.description.abstractenIn recent years, infinite-dimensional methods have been introduced for the Gaussian channels estimation. The aim of this paper is to study the application of similar methods to Poisson channels. In particular we compute the Bayesian estimator of a Poisson channel using the likelihood ratio and the discrete Malliavin gradient. This algorithm is suitable for numerical implementation via the Monte-Carlo scheme. As an application we provide an new proof of the formula obtained recently by Guo, Shamai and Verdu\'u relating some derivatives of the input-output mutual information of a time-continuous Poisson channel and the conditional mean estimator of the input. These results are then extended to mixed Gaussian-Poisson channels.en
dc.relation.isversionofjnlnameIEEE Transactions on Information Theory
dc.relation.isversionofjnlvol59
dc.relation.isversionofjnlissue10
dc.relation.isversionofjnldate2013
dc.relation.isversionofjnlpages6261-6272
dc.relation.isversionofdoihttp://dx.doi.org/10.1109/TIT.2013.2268911
dc.identifier.urlsitehttp://arxiv.org/abs/0709.1211v3en
dc.description.sponsorshipprivateouien
dc.relation.isversionofjnlpublisherIEEE
dc.subject.ddclabelProbabilités et mathématiques appliquéesen


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