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

Gilles Celeux 1
1 SELECT - Model selection in statistical learning
LMO - Laboratoire de Mathématiques d'Orsay, Inria Saclay - Ile de France
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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https://hal.inria.fr/hal-00927006
Contributor : Gilles Celeux <>
Submitted on : Friday, January 10, 2014 - 4:06:55 PM
Last modification on : Wednesday, October 14, 2020 - 3:56:56 AM

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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, SOCIETÀ ITALIANA DI STATISTICA, Jun 2013, Brescia, Italy. ⟨hal-00927006⟩

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