Mixture models

Christophe Biernacki 1, 2
2 MODAL - MOdel for Data Analysis and Learning
LPP - Laboratoire Paul Painlevé - UMR 8524, Inria Lille - Nord Europe, CERIM - Santé publique : épidémiologie et qualité des soins-EA 2694, Polytech Lille, Université de Lille 1, IUT’A
Abstract : Finite mixture models are one of the probabilistic frameworks which reach an especially diverse community of people, including statisticians and practitioners (scientific or not). Initial reasons for being confronted with mixtures may be different for impacted communities but lead finally to close interconnections between them. Indeed, applied statisticians and practitioners usually discover finite mixture models from the numerous application fields where they meet numerous successes. It typically gathers {none,un,semi-} supervised classification and density estimation. The keys of these successes are both their high meaningfulness and flexibility. However, flexibility is in return a matter of algorithmic and mathematical questionings for methodological and theoretical statisticians. In particular, it addresses estimation and model selection issues, on both computational and mathematical aspects. But, solutions to be provided to these issues highly beneficiate to depend on initial related application fields.
Type de document :
Chapitre d'ouvrage
J-J. Droesbeke; G. Saporta; C. Thomas-Agnan. Choix de modèles et agrégation, Technip, 2017, 9782710811770. 〈http://www.editionstechnip.com/fr/catalogue-detail/2247/model-choice-and-model-aggregation.html〉
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Soumis le : lundi 18 janvier 2016 - 11:36:18
Dernière modification le : mardi 3 juillet 2018 - 11:26:58
Document(s) archivé(s) le : mardi 19 avril 2016 - 10:12:28

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Christophe Biernacki. Mixture models. J-J. Droesbeke; G. Saporta; C. Thomas-Agnan. Choix de modèles et agrégation, Technip, 2017, 9782710811770. 〈http://www.editionstechnip.com/fr/catalogue-detail/2247/model-choice-and-model-aggregation.html〉. 〈hal-01252671〉

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