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dc.contributor.authorRobert, Christian P.
dc.contributor.authorGrazian, Clara
dc.date.accessioned2014-06-11T14:12:05Z
dc.date.available2014-06-11T14:12:05Z
dc.date.issued2014
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/13436
dc.language.isoenen
dc.subjectObjective Bayesen
dc.subjectMixture modelsen
dc.subjectJeffreys prioren
dc.subject.ddc519en
dc.subject.classificationjelC11en
dc.titleJeffreys Priors for Mixture Modelsen
dc.title.alternativeDistribuzioni a priori di Jeffreys per i modelli misturaen
dc.typeCommunication / Conférence
dc.description.abstractenMixture models may be a useful and flexible tool to describe data with a complicated structure, for instance characterized by multimodality or asymmetry. In a Bayesian setting, it is a well established fact that one need to be careful in using improper prior distributions, since the posterior distribution may not be proper. This feature leads to problems in carry out an objective Bayesian approach. In this work an analysis of Jeffreys priors in the setting of finite mixture models will be presented.en
dc.description.abstractotherlanguageI modelli mistura sono uno strumento utile e flessibile per descrivere dati dalla struttura complicata, ad esempio multimodale o asimmetrica. In am- bito Bayesiano, ` e un fatto noto in letteratura che sia necessario essere attenti con l’utilizzo di distribuzioni a priori improprie, dal momento che la distribuzione a pos- teriori potrebbe non essere propria. Purtroppo, questa caratteristica rende difficile un approccio Bayesiano oggettivo. In questo lavoro, verr ` a presentata un’analisi dei risultati ottenuti utilizzando distribuzioni a priori (non informative) di Jeffreys.en
dc.identifier.citationpages6en
dc.identifier.urlsitehttp://www.sis2014.it/proceedings/allpapers/2891.pdfen
dc.subject.ddclabelProbabilités et mathématiques appliquéesen
dc.relation.conftitleSIS 2014en
dc.relation.confdate2014-06
dc.relation.confcityCagliarien
dc.relation.confcountryItalieen
dc.relation.forthcomingnonen


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