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Computational Solutions for Bayesian Inference in Mixture Models
Book chapter

Computational Solutions for Bayesian Inference in Mixture Models

Christian Robert, Gilles Celeux, Kaniav Kamary, Gertraud Malsiner-Walli and Jean-Michel Marin
Handbook of Mixture Analysis
12/2018

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'.
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