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.
General
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