part PART ONE Model structure, properties and methods -- chapter 1 Preliminaries: mixtures and Markov chains -- chapter 2 Hidden Markov models: definition and properties -- chapter 3 Estimation by direct maximization of the likelihood -- chapter 4 Estimation by the EM algorithm -- chapter 5 Forecasting, decoding and state prediction -- chapter 6 Model selection and checking -- chapter 7 Bayesian inference for Poisson–HMMs -- chapter 8 Extensions of the basic hidden Markov model -- part PART TWO Applications -- chapter 9 Epileptic seizures -- chapter 10 Eruptions of the Old Faithful geyser -- chapter 11 Drosophila speed and change of direction -- chapter 12 Wind direction at Koeberg -- chapter 13 Models for financial series -- chapter 14 Births at Edendale Hospital -- chapter 15 Homicides and suicides in Cape Town, 1986–1991 -- chapter 16 Animal behaviour model with feedback.