Bayesian estimation of dynamic finite mixtures

The paper introduces an algorithm for estimation of dynamic mixture models. A new feature of the proposed algorithm is the ability to consider a dynamic form not only for component models but also for the pointer model, which describes the activities of the mixture components in time. The pointer model is represented by a table of transition probabilities that stochastically control the switching between the active components in dependence on the last active one. This feature brings the mixture model closer to real multi‐modal systems. It can also serve for a prediction of the future behavior of the modeled system. Copyright © 2011 John Wiley & Sons, Ltd.