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.

Derived from symposia on hidden Markov models (HMMs) at a SIAM (Society for Industrial and Applied Mathematics) Conference on Applications of Dynamical Systems, this text introduces HMMs as "special cases of discrete time-space models characterized by a state transition probability function and an observation probability function..." Fraser (space and remote sensing sciences, Los Alamos National Laboratory) presents the basic theory behind and codes for implementing the algorithms presented, analogies to dynamical systems, and a practical application to solve a problem in medicine. Formulas for matrices and Gaussians, and notes on the dependencies required for the software are appended. A background in undergraduate linear algebra, probability, and differential equations are prerequisites. Annotation ©2008 Book News, Inc., Portland, OR (booknews.com)