How useful Bayesian inference could be in Model-based clustering?

Gilles Celeux 1
1 SELECT - Model selection in statistical learning
Inria Saclay - Ile de France, LMO - Laboratoire de Mathématiques d'Orsay, CNRS - Centre National de la Recherche Scientifique : UMR
Abstract : In this communication, we analyse the pro and the con of Bayesian inference in the model-based clustering context. We exhibit situations where its main drawbacks can be avoided or circumvented. We consider the latent class model for categorical data and derive their (completed) integrated likelihoods without requiring asymptotic approximations. We highlight the interest and the traps of the resulting model selection criteria.
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Communication dans un congrès
Advances in Latent Variables-Methods, Models and Applications, Jun 2013, Brescia, Italy. 2013
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https://hal.inria.fr/hal-00927006
Contributeur : Gilles Celeux <>
Soumis le : vendredi 10 janvier 2014 - 16:06:55
Dernière modification le : jeudi 11 janvier 2018 - 06:22:14

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  • HAL Id : hal-00927006, version 1

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Gilles Celeux. How useful Bayesian inference could be in Model-based clustering?. Advances in Latent Variables-Methods, Models and Applications, Jun 2013, Brescia, Italy. 2013. 〈hal-00927006〉

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