Fig. 1.

Graphical representation of three DMA mixture models. (a) Independent case. (b) The MDI model. In both (a) and (b), denotes the observation in dataset k and is generated by mixture component . The prior probabilities associated with the distinct component allocation variables, , are given in the vector , which is itself assigned a symmetric Dirichlet prior with parameter . The parameter vector, , for component c in dataset k is assigned a prior. In (b), we additionally have parameters, each of which models the dependence between the component allocations of observations in dataset k and