Bayesian online algorithms for learning in discrete hidden Markov models
Roberto C. Alamino, Nestor Caticha · Discrete and Continuous Dynamical Systems - B · 2008
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.