Dealing with label switching under model uncertainty

Statistical mixture distributions are used to model scenarios in which certain variables are measured but a categorical variable is missing. For example, although clinical data on a patient may be available their disease category may not be, and this adds significant degrees of complication to the statistical analysis. The above situation characterises the simplest mixture-type scenario; variations include, among others, hidden Markov models, in which the missing variable follows a Markov chain model, and latent structure models, in which the missing variable or variables represent model-enriching devices rather than real physical entities. In the title of the workshop the term `mixture' is taken to include these and other variations along with the simple mixture. The motivating factors for this three-day workshop are that research on inference and computational techniques for mixture-type models is currently experiencing major advances and that simultaneously the application of mixture modelling to many fields in science and elsewhere has never been so rich. We thus assembling top players, from statistics and computer science, in both methodological research and applied inference at this fertile interface. The methodological component will involve both Bayesian and non-Bayesian contributions and biology and economics will feature strongly among the application areas to be covered.