Computational Solutions for Bayesian Inference in Mixture Models

2018/12/18 by Gilles Celeux, Kaniav Kamary, Celeux, Gilles +9 · 2 citations
Chemistry · Computer Science · Decision Sciences · Mathematics · #Artificial intelligence #Bayesian Methods and Mixture Models #Bayesian inference #Bayesian probability #Chemistry #Computation (stat.CO) #Computational chemistry #Computer science #Econometrics #FOS: Computer and information sciences #Gaussian #Gibbs sampling #Inference #Markov Chains and Monte Carlo Methods #Markov chain Monte Carlo #Mathematics #Mixture model #Monte Carlo method #Optimal Experimental Design Methods #Physics #Posterior probability #Robustness (evolution) #Statistical physics #Statistics #stat.CO

paper · pdf · open access · doi:10.48550/arxiv.1812.07240

published in WU Research

arxiv created 2018/12/18 · openalex publication_date 2018/12/18 · arxiv updated 2018/12/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01

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