Hidden Markov Models for Time Series: An Introduction Using R

Description

Content: Preface Preface to rst edition Notation and abbreviations Part I: Model structure, properties and methods Chapter 1: Preliminaries: mixtures and Markov chains Chapter 2: Hidden Markov models: de nition 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{hidden Markov models Chapter 8: R packages Part II: Extensions Chapter 9: HMMs with general state-dependent distributionChapter 10: Covariates and other extra dependencies Chapter 11: Continuous-valued state processes Chapter 12: Hidden semi-Markov models and their representation as HMMs Chapter 13: HMMs for longitudinal data Part III: Applications Chapter 14: Introduction to applications Chapter 15: Epileptic seizures Chapter 16: Daily rainfall occurrence Chapter 17: Eruptions of the Old Faithful geyser Chapter 18: HMMs for animal movement Chapter 19: Wind direction at Koeberg Chapter 20: Models for nancial series Chapter 21: Births at Edendale HospitalChapter 22: Homicides and suicides in Cape Town, 1986{1991 Chapter 23: A model for animal behaviour which incorporates feedback Chapter 24: Estimating the survival rates of Soay sheep from mark{recapture{recovery data Appendix A: Examples of R code Appendix B: Some proofs References