Computational Solutions for Bayesian Inference in Mixture Models

Christian Robert (1, 2, 3) , Gilles Celeux (4) , Kaniav Kamary (1) , Gertraud Malsiner-Walli (5) , Jean-Michel Marin (6)

1 CEREMADE - CEntre de REcherches en MAthématiques de la DEcision
2 University of Warwick [Coventry]
3 CREST-INSEE - Centre de Recherche en Economie et en Statistique
4 SELECT - Model selection in statistical learning
5 Vienna University of Economics and Business
6 UM - Université de Montpellier

This chapter surveys the most standard Monte Carlo methods available for simulating from a posterior distribution associated with a mixture and conducts some experiments about the robustness of the Gibbs sampler in high dimensional Gaussian settings. This is a chapter prepared for the forthcoming 'Handbook of Mixture Analysis'.

Domaines: Modélisation et simulation; Statistiques [math.ST]

Christian Robert, Gilles Celeux, Kaniav Kamary, Gertraud Malsiner-Walli, Jean-Michel Marin. Computational Solutions for Bayesian Inference in Mixture Models. Handbook of Mixture Analysis, CRC Press, 2018. ⟨hal-01961038⟩