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Computational Solutions for Bayesian Inference in Mixture Models

Robert, Christian P.; Celeux, Gilles; Kamary, Kaniav; Malsiner-Walli, Gertraud; Marin, Jean-Michel (2019), Computational Solutions for Bayesian Inference in Mixture Models, in Sylvia Fruhwirth-Schnatter, Gilles Celeux, Christian P. Robert, Handbook of Mixture Analysis, CRC Press, Taylor & Francis, p. 24

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Type
Chapitre d'ouvrage
Date
2019
Book title
Handbook of Mixture Analysis
Book author
Sylvia Fruhwirth-Schnatter, Gilles Celeux, Christian P. Robert
Publisher
CRC Press, Taylor & Francis
ISBN
9781498763813
Number of pages
498
Pages
24
Metadata
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Author(s)
Robert, Christian P.
CEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
Celeux, Gilles
INRIA Rocquencourt
Kamary, Kaniav
CEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
Malsiner-Walli, Gertraud
Institute for Information Business, Wirtschaftsuniversität Wien [Institute for Information Business, Wirtschaftsuniversität Wien (WU Vienna)]
Marin, Jean-Michel cc
Institut de Mathématiques et de Modélisation de Montpellier [I3M]
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'.
Subjects / Keywords
Bayesian Inference; Mixture Models