Hidden Markov Models

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.

• 93E11, 93E20, 60G35, 60H30
• EM algorithm
• Hidden Markov chains
• filtering
• parameter estimation
• stochastic control
• Brownian motion
• dynamics
• equation
• hidden Markov model
• Markov chain
• Markov model
• Markov process
• optimal control
• Parameter
• Rang
• quantitative finance

• Haskayne School of Business Scurfield Hall, University of Calgary, Calgary, Canada

  Robert J. Elliott

• Department of Systems Engineering and Cooperative Research Centre for Robust and Adaptive Systems Research School of Physical Sciences and Engineering, Australian National University, Canberra, Australia

  John B. Moore

• Department of Mathematics and Statistics College of Science, Sultan Qaboos University, Sultanate of Oman

  Lakhdar Aggoun

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