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dc.contributor.authorNur, Darfiana*
dc.contributor.authorAllingham, David*
dc.contributor.authorRousseau, Judith*
dc.contributor.authorMengersen, Kerrie*
dc.contributor.authorMcVinish, Ross*
dc.date.accessioned2009-06-26T10:41:17Z
dc.date.available2009-06-26T10:41:17Z
dc.date.issued2009
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/501
dc.language.isoenen
dc.subjectDNA sequence; hidden Markov model; Bayesian model; sensitivity analysis; α-fetoprotein; Markov chain Monte Carlo; importance sampling.en
dc.subject.ddc519en
dc.titleBayesian hidden Markov Model for DNA segmentation : A prior sensitivity analysisen
dc.typeArticle accepté pour publication ou publié
dc.contributor.editoruniversityotherQueensland University of Technology;Australie
dc.contributor.editoruniversityotherUniversity of Newcastle;Australie
dc.description.abstractenThe focus of this paper is on the sensitivity to the specification of the prior in a hidden Markov model describing homogeneous segments of DNA sequences. An intron from the chimpanzee α-fetoprotein gene, which plays an im- portant role in embryonic development in mammals is analysed. Three main aims are considered : (i) to assess the sensitivity to prior specification in Bayesian hidden Markov models for DNA sequence segmentation; (ii) to examine the impact of replacing the standard Dirichlet prior with a mixture Dirichlet prior; and (iii) to propose and illus- trate a more comprehensive approach to sensitivity analysis, using importance sampling. It is obtained that (i) the posterior estimates obtained under a Bayesian hidden Markov model are indeed sensitive to the specification of the prior distributions; (ii) compared with the standard Dirichlet prior, the mixture Dirichlet prior is more flexible, less sensitive to the choice of hyperparameters and less constraining in the analysis, thus improving posterior estimates; and (iii) importance sampling was computationally feasible, fast and effective in allowing a richer sensitivity analysis.en
dc.relation.isversionofjnlnameComputational Statistics and Data Analysis
dc.relation.isversionofjnlvol53en
dc.relation.isversionofjnlissue5en
dc.relation.isversionofjnldate2009-03
dc.relation.isversionofjnlpages1873-1882en
dc.relation.isversionofdoihttp://dx.doi.org/10.1016/j.csda.2008.07.007en
dc.identifier.urlsitehttp://hal.archives-ouvertes.fr/hal-00328181/en/en
dc.description.sponsorshipprivateouien
dc.subject.ddclabelProbabilités et mathématiques appliquéesen
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