On Bayesian Analysis of Mixtures with an Unknown Number of Components (with discussion)
成分数量未知的混合模型贝叶斯分析（附讨论）
Summary New methodology for fully Bayesian mixture analysis is developed, making use of reversible jump Markov chain Monte Carlo methods that are capable of jumping between the parameter subspaces corresponding to different numbers of components in the mixture. A sample from the full joint distribution of all unknown variables is thereby generated, and this can be used as a basis for a thorough presentation of many aspects of the posterior distribution. The methodology is…