Bayesian mixtures of multiple scale distributions

Abstract : Multiple scale distributions are multivariate distributions that exhibit a variety of shapes not necessarily elliptical while remaining analytical and tractable. In this work we consider mixtures of such distributions for their ability to handle non standard typically non-gaussian clustering tasks. We propose a Bayesian formulation of the mixtures and a tractable inference procedure based on variational approximation. The interest of such a Bayesian formulation is illustrated on an important mixture model selection task, which is the issue of selecting automatically the number of components. We derive promising procedures that can be carried out in a single-run, in contrast to the more costly comparison of information criteria. Preliminary results on simulated and real data show promising performance in terms of clustering and computation time.
Type de document :
Pré-publication, Document de travail
Liste complète des métadonnées
Contributeur : Florence Forbes <>
Soumis le : mercredi 12 décembre 2018 - 21:32:38
Dernière modification le : mercredi 26 décembre 2018 - 10:28:34
Document(s) archivé(s) le : mercredi 13 mars 2019 - 16:07:02


Fichiers produits par l'(les) auteur(s)


  • HAL Id : hal-01953393, version 1



Alexis Arnaud, Florence Forbes, Russel Steele, Benjamin Lemasson, Emmanuel Barbier. Bayesian mixtures of multiple scale distributions. 2018. 〈hal-01953393〉



Consultations de la notice


Téléchargements de fichiers