• Birth-and-death process;
  • Classification;
  • Galaxy data;
  • Heterogeneity;
  • Lake acidity data;
  • Markov chain Monte Carlo method;
  • Normal mixtures;
  • Predictive distribution;
  • Reversible jump algorithms;
  • Sensitivity analysis

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 applied here to the analysis of univariate normal mixtures, using a hierarchical prior model that offers an approach to dealing with weak prior information while avoiding the mathematical pitfalls of using improper priors in the mixture context.