Quotation Celeux, Gilles, Kamary, Kaniav, Malsiner-Walli, Gertraud, Marin, Jean-Michel, Robert, Christian P. 2019. Computational solutions for Bayesian inference in mixture models. In: Handbook of Mixture Analysis, Hrsg. Sylvia Frühwirth-Schnatter, Gilles Celeux, Christian P. Robert, 73-96. Boca Raton, Florida: Chapman & Hall.


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Abstract

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.

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Publication's profile

Status of publication Published
Affiliation WU
Type of publication Chapter in edited volume
Language English
Title Computational solutions for Bayesian inference in mixture models
Title of whole publication Handbook of Mixture Analysis
Editor Sylvia Frühwirth-Schnatter, Gilles Celeux, Christian P. Robert
Page from 73
Page to 96
Location Boca Raton, Florida
Publisher Chapman & Hall
Year 2019
Reviewed? Y
ISBN 978-1498763813
Open Access N

Associations

Projects
Shrinking and Regularizing Finite Mixture Models
People
Malsiner-Walli, Gertraud (Details)
External
Celeux, Gilles (INRIA Saclay, France)
Kamary, Kaniav (INRIA Saclay, France)
Marin, Jean-Michel (Universite´ de Montpellier, France)
Robert, Christian P. (Universite´ Paris-Dauphine, France)
Organization
Institute for Statistics and Mathematics IN (Details)
Research areas (ÖSTAT Classification 'Statistik Austria')
1162 Statistics (Details)
5701 Applied statistics (Details)
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