EXAMPLES OF BAYESIAN MIXTURE MODELS Mixture models can be used to model complex distributions of "target" values, without any dependence on input values. The mixture components that are found by the model might also be interpretable as representing underlying "latent classes" in the data. Examples are given here of how this can be done for binary and for real-valued data. The data and command files for these examples are in the "ex-mix" directory. This directory also contains the data and command files used for the example in my tech report on "Markov chain sampling methods for Dirichlet process mixture models".