Hidden Markov Models: Estimation and Control (Stochastic Modelling and Applied Probability, 29)
Language: English
Published by Springer, 1994
- Hardcover
- New
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Seller Inventory # Q-0387943641
- Title
- Hidden Markov Models: Estimation and Control (Stochastic Modelling and Applied Probability, 29)
- Author
- Elliott, Robert J; Aggoun, Lakhdar; Moore, John B.
- Publisher
- Springer
- Publication year
- 1994
- Condition
- New
- Binding
- hardcover
- Language
- English
- ISBN 10
- 0387943641
- ISBN 13
- 9780387943640
- Edition
- 3rd Edition
- Item weight
- 25 ounces
- Dimensions
- 6x0x9
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.
"Synopsis" may belong to another edition of this title.
From the Back Cover
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.
"About the title" may belong to another edition of this title.
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