Hidden Markov Models and Dynamical Systems

Presents algorithms for using HMMs and explains the derivation of those algorithms for the dynamical systems community.

Andrew M . Fraser (Author)

9780898716658

Paperback / softback, published 30 March 2008

144 pages
22.9 x 15.2 x 2 cm, 0.277 kg

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.

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.

Subject Areas: Miscellaneous items [WZ]