Book Sections Year : 2018

Computational Solutions for Bayesian Inference in Mixture Models

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. This is a chapter prepared for the forthcoming 'Handbook of Mixture Analysis'.

Dates and versions

hal-01961038 , version 1 (19-12-2018)

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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⟩
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