Skip to Main content Skip to Navigation
Conference papers

Component elimination strategies to fit mixtures of multiple scale distributions

Florence Forbes 1 Alexis Arnaud 1 Benjamin Lemasson 2 Emmanuel Barbier 2
1 MISTIS - Modelling and Inference of Complex and Structured Stochastic Systems
Inria Grenoble - Rhône-Alpes, Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology, LJK - Laboratoire Jean Kuntzmann
Abstract : We address the issue of selecting automatically the number of components in mixture models with non-Gaussian components. As a more efficient alternative to the traditional comparison of several model scores in a range, we consider procedures based on a single run of the inference scheme. Starting from an overfitting mixture in a Bayesian setting, we investigate two strategies to eliminate superfluous components. We implement these strategies for mixtures of multiple scale distributions which exhibit a variety of shapes not necessarily elliptical while remaining analytical and tractable in multiple dimensions. A Bayesian formulation and a tractable inference procedure based on variational approximation are proposed. Preliminary results on simulated and real data show promising performance in terms of model selection and computational time.
Document type :
Conference papers
Complete list of metadata

Cited literature [32 references]  Display  Hide  Download
Contributor : Florence Forbes Connect in order to contact the contributor
Submitted on : Monday, December 16, 2019 - 9:56:22 PM
Last modification on : Wednesday, November 3, 2021 - 5:10:46 AM
Long-term archiving on: : Tuesday, March 17, 2020 - 10:36:50 PM


Files produced by the author(s)




Florence Forbes, Alexis Arnaud, Benjamin Lemasson, Emmanuel Barbier. Component elimination strategies to fit mixtures of multiple scale distributions. RSSDS 2019 - Research School on Statistics and Data Science, Jul 2019, Melbourne, Australia. pp.81-95, ⟨10.1007/978-981-15-1960-4_6⟩. ⟨hal-02415090⟩



Les métriques sont temporairement indisponibles