University of Washington - Department of Statistics
Recently there has been a flurry of activity in the area of hidden Markov models. Applications have been in speech recognition, neurophysiology, hydrology, astronomy, geosciences, and hematology, among others. The general structure of hidden Markov models (and their relation to state space models in time series analysis) is outlined. Estimation methods (exact and by Monte Carlo) are outlined, and the methodology is exemplified with applications from several of the fields mentioned above.