Bayesian mixtures of multiple scale distributions - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Preprints, Working Papers, ... Year : 2019

Bayesian mixtures of multiple scale distributions


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.
Fichier principal
Vignette du fichier
BayesMST-3.pdf (9.32 Mo) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-01953393 , version 1 (12-12-2018)
hal-01953393 , version 2 (08-07-2019)
hal-01953393 , version 3 (27-09-2019)


  • HAL Id : hal-01953393 , version 3


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


Gmail Facebook X LinkedIn More