Title :
A Bayesian approach for classification of Markov sources
Author :
Merhav, Neri ; Ziv, Jacob
Author_Institution :
Dept. of Electr. Eng., Technion-Israel Inst. of Technol., Haifa, Israel
fDate :
7/1/1991 12:00:00 AM
Abstract :
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
Keywords :
Bayes methods; Markov processes; information theory; parameter estimation; Bayesian approach; Markov sources; classification; minimum error probability; parameter estimation; sequential estimation; universal classifier; Bayesian methods; Error probability; Helium; Jacobian matrices; Parameter estimation; Random variables; Sequential analysis; Speech recognition; Testing; Training data;
Journal_Title :
Information Theory, IEEE Transactions on
