A new method for learning imprecise hidden Markov models

Abstract

We present a method for learning imprecise local uncertainty models in stationary hidden Markov models. If there is enough data to justify precise local uncertainty models, then existing learning algorithms, such as the Baum-Welch algorithm, can be used. When there is not enough evidence to justify precise models, the method we suggest here has a number of interesting features. © 2012 Springer-Verlag Berlin Heidelberg.