Computational Solutions for Bayesian Inference in Mixture Models

Thumbnail
View/Open
Date
2019
Dewey
Analyse
Sujet
Bayesian Inference; Mixture Models
Book title
Handbook of Mixture Analysis
Author
Sylvia Fruhwirth-Schnatter, Gilles Celeux, Christian P. Robert
Publisher
CRC Press, Taylor & Francis
Year
01-2019
Pages number
498
ISBN
9781498763813
URI
https://basepub.dauphine.fr/handle/123456789/18582
Collections
Metadata
Show full item record
Author
Robert, Christian P.
60 CEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
Celeux, Gilles
34587 INRIA Rocquencourt
Kamary, Kaniav
60 CEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
Malsiner-Walli, Gertraud
420887 Institute for Information Business, Wirtschaftsuniversität Wien [Institute for Information Business, Wirtschaftsuniversität Wien (WU Vienna)]
Marin, Jean-Michel
631 Institut de Mathématiques et de Modélisation de Montpellier [I3M]
Type
Chapitre d'ouvrage
Item number of pages
24
Abstract (EN)
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'.