• mixture model;
  • Bayesian estimation;
  • clustering;
  • classification;
  • working point detection and prediction


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.