An experimental study of coupled hidden Markov models
In this work, ail experimental study of the coupled hidden Markov models (CHMMs) is carried out. CHMMs are directed graphical models of stochastic processes and are a special type of Dynamic Bayesian Networks (DBNs). The DBNs generalize the hidden Markov models by representing the hidden states as state variables, and allow the states to have complex interdependencies. This study considers the probabilistic inference problem and the learning problem of these models. A series of experiments were carried out to evaluate the relationship between the learning outcome and various factors in the learning process. In addition, this study looks into the capabilities of the CHMMs in the context of Maximum Likelihood classification of temporal patterns. Empirical results suggest that even with perfect learning, the classification error can be significant in some cases, and it is important to limit the state space of the models when considering the framework in real-world applications.