Latent variable models have been used extensively in the social
sciences. In this work a latent class analysis is used to identify syndromes
within Alzheimer's disease. The fitting of the model is done in a Bayesian
framework, and this is examined in detail here. In particular, the label
switching problem is identified, and solutions presented. Graphical
summaries of the posterior distribution are included
