Maximum likelihood estimation for multivariate observations of Markov sources

Authors:
L. Liporace
Affiliations:
-
Venue:
IEEE Transactions on Information Theory
Year:
2006

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Abstract

Parameter estimation for multivariate functions of Markov chains, a class of versatile statistical models for vector random processes, is discussed. The model regards an ordered sequence of vectors as noisy multivariate observations of a Markov chain. Mixture distributions are a special case. The foundations of the theory presented here were established by Baum, Petrie, Soules, and Weiss. A powerful representation theorem by Fan is employed to generalize the analysis of Baum, {em et al.} to a larger class of distributions.