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Mixture models

Christophe Biernacki 1, 2 
2 MODAL - MOdel for Data Analysis and Learning
LPP - Laboratoire Paul Painlevé - UMR 8524, Université de Lille, Sciences et Technologies, Inria Lille - Nord Europe, METRICS - Evaluation des technologies de santé et des pratiques médicales - ULR 2694, Polytech Lille - École polytechnique universitaire de Lille
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.
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https://hal.inria.fr/hal-01252671
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Submitted on : Monday, January 18, 2016 - 11:36:18 AM
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Christophe Biernacki. Mixture models. J-J. Droesbeke; G. Saporta; C. Thomas-Agnan. Choix de modèles et agrégation, Technip, 2017, 9782710811770. ⟨hal-01252671⟩

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