Computational Solutions for Bayesian Inference in Mixture Models

2018/12/18 by Celeux, Gilles, Kamary, Kaniav, Malsiner-Walli, Gertraud +2 · 1 citation
#Computation (stat.CO) #FOS: Computer and information sciences

paper · doi:10.48550/arxiv.1812.07240

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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Related

• Bayesian computational methods
• Fast sampling and model selection for Bayesian mixture models
• Bayesian Inference on Mixtures of Distributions
• Mixture model fitting using conditional models and modal Gibbs sampling
• Bayesian inference of Gaussian mixture models with noninformative priors