Hidden Markov Models Estimation and Control

About this book

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 Hidden Markov Models: Estimation and Control 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.

Contents

Preface

Part I Introduction
1.Hidden Markov Model Processing

Part II Discrete-Time HMM Estimation
2.Discrete States and Discrete Observations
3.Continuous-Range Observations
4.Continuous-Range States and Observations
5.A General Recursive Filter
6.Practical Recursive Filters

Part III Continuous-Time HMM Estimation
7.Discrete-Range States and Observations
8.Markov Chains in Brownian Motion

Part IV Two-Dimensional HMM Estimation
9.Hidden Markov Random Fields

Part V HMM Optimal Control
10.Discrete-Time HMM Control
11.Risk-Sensitive Control of HMM
12.Continuous-Time HMM Control

Appendices
A. Basic Probability Concepts
B. Continuous-Time Martingale Representation

References
Index

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