Hidden Markov models: estimation and control

Robert J. Elliott, John B. Moore, Lakhdar Aggoun (auth.)0387943641, 9780387943640

As more applications are found, interest in Hidden Markov Models continues to grow. Following comments and feedback from colleagues, students and other working with Hidden Markov Models the corrected 3rd printing of this volume contains clarifications, improvements and some new material, including results on smoothing for linear Gaussian dynamics.

In Chapter 2 the derivation of the basic filters related to the Markov chain are each presented explicitly, rather than as special cases of one general filter. Furthermore, equations for smoothed estimates are given. The dynamics for the Kalman filter are derived as special cases of the authors’ general results and new expressions for a Kalman smoother are given. The Chapters on the control of Hidden Markov Chains are expanded and clarified. The revised Chapter 4 includes state estimation for discrete time Markov processes and Chapter 12 has a new section on robust control.

Table of contents :
Front Matter….Pages i-xiv
Front Matter….Pages 1-1
Hidden Markov Model Processing….Pages 3-11
Front Matter….Pages 13-13
Discrete States and Discrete Observations….Pages 15-53
Continuous-Range Observations….Pages 55-81
Continuous-Range States and Observations….Pages 83-141
A General Recursive Filter….Pages 143-162
Practical Recursive Filters….Pages 163-194
Front Matter….Pages 195-195
Discrete-Range States and Observations….Pages 197-212
Markov Chains in Brownian Motion….Pages 213-233
Front Matter….Pages 235-235
Hidden Markov Random Fields….Pages 237-270
Front Matter….Pages 271-271
Discrete-Time HMM Control….Pages 273-289
Risk-Sensitive Control of HMM….Pages 291-314
Continuous-Time HMM Control….Pages 315-349
Back Matter….Pages 351-377