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Estimation de modèles de mélanges gaussiens univariés à partir de données groupées dans le cas d'une grande volumétrie de données

Filippo Antonazzo 1 Christophe Biernacki 1 Christine Keribin 2
1 MODAL - MOdel for Data Analysis and Learning
Inria Lille - Nord Europe, LPP - Laboratoire Paul Painlevé - UMR 8524, METRICS - Evaluation des technologies de santé et des pratiques médicales - ULR 2694, Polytech Lille - École polytechnique universitaire de Lille, Université de Lille, Sciences et Technologies
2 CELESTE - Statistique mathématique et apprentissage
Inria Saclay - Ile de France, LMO - Laboratoire de Mathématiques d'Orsay
Abstract : Popularity of unsupervised learning is magnified by the regular increase of sample sizes. Indeed, it provides opportunity to reveal information previously out of scope. However, the volume of data leads to some issues related to prohibitive calculation times and also to high energy consumption and the need of high computational ressources. Resorting to binned data depending on an adaptive grid is expected to give proper answer to such green computing issues while not harming the related estimation issues. A first attempt is conducted in the context of univariate Gaussian mixtures, included a numerical illustration and some theoretical advances.
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https://hal.inria.fr/hal-03082437
Contributor : Christine Keribin <>
Submitted on : Friday, December 18, 2020 - 3:50:29 PM
Last modification on : Tuesday, January 5, 2021 - 10:43:44 AM

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Filippo Antonazzo, Christophe Biernacki, Christine Keribin. Estimation de modèles de mélanges gaussiens univariés à partir de données groupées dans le cas d'une grande volumétrie de données. SFdS 2020 - 52èmes Journées de Statistiques de la Société Française de Statistique, May 2020, Nice, France. ⟨hal-03082437⟩

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