Bayesian online algorithms for learning in discrete hidden Markov models | Zendy

Roberto C. Alamino | Zendy; Nestor Caticha | Zendy

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Bayesian online algorithms for learning in discrete hidden Markov models

Author(s) -

Roberto C. Alamino,

Nestor Caticha

Publication year - 2008

Publication title -

discrete and continuous dynamical systems - b

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.864

H-Index - 53

eISSN - 1553-524X

pISSN - 1531-3492

DOI - 10.3934/dcdsb.2008.9.1

Subject(s) - generalization , variable order bayesian network , divergence (linguistics) , bayesian probability , computer science , hidden markov model , algorithm , artificial intelligence , markov chain , machine learning , generalization error , measure (data warehouse) , markov model , maximum entropy markov model , variable order markov model , mathematics , bayesian inference , artificial neural network , data mining , mathematical analysis , linguistics , philosophy

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.

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