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Bayesian online algorithms for learning in discrete hidden Markov models

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  • We propose and analyze two different Bayesian online algorithms for learning in discrete Hidden Markov Models and compare their performance with the already known Baldi-Chauvin Algorithm. Using the Kullback-Leibler divergence as a measure of generalization we draw learning curves in simplified situations for these algorithms and compare their performances.
    Mathematics Subject Classification: Primary: 68T05; Secondary: 60J20, 62F15.

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