Bayesian online algorithms for learning in discrete Hidden Markov Models

Roberto C. Alamino, Nestor Caticha

Research output: Contribution to journalArticlepeer-review

Abstract

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.
Original languageEnglish
Pages (from-to)1-10
Number of pages10
JournalDiscrete and Dontinuous Dynamical Systems: Series B
Volume9
Issue number1
Publication statusPublished - Jan 2008

Bibliographical note

This is a pre-copy-editing, author-produced PDF of an article accepted for publication in Discrete and Continuous Dynamical Systems Series B following peer review. The definitive publisher-authenticated version Alamino, Roberto C. and Caticha, Nestor (2008) Bayesian online algorithms for learning in Hidden Markov Models. Discrete and Continuous Dynamical Systems Series B , 9 (1). pp. 1-10. ISSN 1531-3492 is available online at: http://aimsciences.org/journals/pdfs.jsp?paperID=2980&mode=abstract

Keywords

  • Bayesian online algorithms
  • discrete Hidden Markov Models
  • Baldi-Chauvin algorithm
  • Kullback-Leibler divergence
  • learning curves

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