Bayesian Inference on Mixtures of Distributions

Abstract

This survey covers state-of-the-art Bayesian techniques for the estimation of mixtures. It complements the earlier Marin, Mengersen and Robert (2005) by studying new types of distributions, the multinomial, latent class and t distributions. It also exhibits closed form solutions for Bayesian inference in some discrete setups. Lastly, it sheds a new light on the computation of Bayes factors via the approximation of Chib (1995).

24 pages

References in corpus (2)

• Properties of Nested Sampling
• On variance stabilisation by double Rao-Blackwellisation

Cited by in corpus (8)

• Testing hypotheses via a mixture estimation model
• Recursive Pathways to Marginal Likelihood Estimation with Prior-Sensitivity Analysis
• Improving the Convergence Properties of the Data Augmentation Algorithm with an Application to Bayesian Mixture Modeling
• Bayesian inference of Gaussian mixture models with noninformative priors
• Control Variates for Reversible MCMC Samplers
• On Bayesian Data Analysis
• Bayesian Computational Tools
• Proximity penalty priors for Bayesian mixture models