Maximum likelihood estimation for mixture multivariate stochastic observations of Markov chains.

Parameter estimation by means of the reestimation algorithm [6] for a class of multivariate mixture density functions of Markov chains is discussed. The scope of the original reestimation algorithm is expanded and the previous assumptions of log-concavity or ellipsoidal symmetry are obviated thereby enhancing the modeling capability of the technique. Reestimation formulas in terms of the well-known forward-backward inductive procedure are also derived.