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Bayesian mixtures of multiple scale distributions

Alexis Arnaud 1 Florence Forbes 1 Russel Steele 2 Benjamin Lemasson 3 Emmanuel Barbier 4
1 MISTIS [2016-2019] - Modelling and Inference of Complex and Structured Stochastic Systems [2016-2019]
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann , Grenoble INP [2007-2019] - Institut polytechnique de Grenoble - Grenoble Institute of Technology [2007-2019]
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.
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Contributor : Florence Forbes <>
Submitted on : Friday, September 27, 2019 - 2:05:10 PM
Last modification on : Tuesday, October 6, 2020 - 12:44:48 PM
Long-term archiving on: : Monday, February 10, 2020 - 4:23:05 AM


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  • HAL Id : hal-01953393, version 3



Alexis Arnaud, Florence Forbes, Russel Steele, Benjamin Lemasson, Emmanuel Barbier. Bayesian mixtures of multiple scale distributions. 2019. ⟨hal-01953393v3⟩



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