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dc.contributor.authorAlston, C.L.*
dc.contributor.authorMengersen, Kerrie*
dc.contributor.authorRobert, Christian P.*
dc.contributor.authorThompson, J.M.*
dc.contributor.authorLittlefield, P.J.*
dc.contributor.authorPerry, D.*
dc.contributor.authorBall, A.J.*
dc.date.accessioned2014-06-02T08:08:33Z
dc.date.available2014-06-02T08:08:33Z
dc.date.issued2007
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/13384
dc.language.isoenen
dc.subjectDensity estimationen
dc.subjectGibbs samplingen
dc.subjectMarkov Chain Monte Carloen
dc.subjectMarkov random fielden
dc.subjectMetropolis–Hastings algorithmen
dc.subjectPosterior simulationen
dc.subjectPerturbation parametersen
dc.subject.ddc519en
dc.titleBayesian mixture models in a longitudinal setting for analysing sheep CAT scan imagesen
dc.typeArticle accepté pour publication ou publié
dc.description.abstractenCAT scanning is used in longitudinal animal science experiments to assess possible changes to carcase composition induced by treatment over given time periods. A hierarchical Bayesian mixture model can be used to analyse the CAT scan data in terms of the proportion of each tissue type present in a scan. In this paper we present an extension to the hierarchical Bayesian mixture model in which estimated parameters from neighbouring CAT scans can be incorporated into the current model. These models are demonstrated using two examples.en
dc.relation.isversionofjnlnameComputational Statistics and Data Analysis
dc.relation.isversionofjnlvol51en
dc.relation.isversionofjnlissue9en
dc.relation.isversionofjnldate2007
dc.relation.isversionofjnlpages4282–4296en
dc.relation.isversionofdoihttp://dx.doi.org/10.1016/j.csda.2006.05.013en
dc.relation.isversionofjnlpublisherElsevieren
dc.subject.ddclabelProbabilités et mathématiques appliquéesen
dc.relation.forthcomingnonen
dc.relation.forthcomingprintnonen
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