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Nouvelles paramétrisations de réseaux Bayésiens et leur estimation implicite - Famille exponentielle naturelle et mélange infini de Gaussiennes

Abstract : Learning a Bayesian network consists in estimating the graph (structure) and the parameters of conditional probability distributions associated with this graph. Bayesian networks learning algorithms rely on classical Bayesian estimation approach whose a priori parameters are often determined by an expert or defined uniformly The core of this work concerns the application of several advances in the field of statistics as implicit estimation, Natural exponential families or infinite mixtures of Gaussian in order to (1) provide new parametric forms for Bayesian networks, (2) estimate the parameters of such models and (3) learn their structure.
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https://tel.archives-ouvertes.fr/tel-00932447
Contributor : Philippe Leray <>
Submitted on : Friday, January 17, 2014 - 10:23:26 AM
Last modification on : Tuesday, June 23, 2020 - 12:30:04 PM
Long-term archiving on: : Friday, April 18, 2014 - 12:45:24 AM

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  • HAL Id : tel-00932447, version 1

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Aida Jarraya Siala. Nouvelles paramétrisations de réseaux Bayésiens et leur estimation implicite - Famille exponentielle naturelle et mélange infini de Gaussiennes. Apprentissage [cs.LG]. Université de Nantes; Faculté des Sciences de Sfax, 2013. Français. ⟨tel-00932447⟩

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