A Bayesian approach for classification of Markov sources

A Bayesian approach for classification of Markov sources whose parameters are not explicitly known is developed and studied. A universal classifier is derived and shown to achieve, within a constant factor, the minimum error probability in a Bayesian sense. The proposed classifier is based on sequential estimation of the parameters of the sources, and it is closely related to earlier proposed universal tests under the Neyman-Pearson criterion.<
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