Description
Society for Industrial & Applied Mathematics,U.S. Hidden Markov Models and Dynamical Systems 2011 Edition by Andrew M. Fraser
This text provides an introduction to hidden Markov models (HMMs) for the dynamical systems community. It is a valuable text for third or fourth year undergraduates studying engineering, mathematics, or science that includes work in probability, linear algebra and differential equations. The book presents algorithms for using HMMs, and it explains the derivation of those algorithms. It presents Kalman filtering as the extension to a continuous state space of a basic HMM algorithm. The book concludes with an application to biomedical signals. This text is distinctive for providing essential introductory material as well as presenting enough of the theory behind the basic algorithms so that the reader can use it as a guide to developing their own variants. Table of contents :- Preface; 1. Introduction; 2. Basic algorithms; 3. Variants and generalizations; 4. Continuous states and observations and Kalman filtering; 5. Performance bounds and a toy problem; 6. Obstructive sleep apnea; Appendix A. Formulas for matrices and Gaussians; Appendix B. Notes on software; Bibliography; Index.