Hidden Markov Models : Estimation and Control

Robert J Elliott

ISBN 10: 1441928413 / ISBN 13: 9781441928412

Editore: Springer New York, Springer US, 2010

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Druck auf Anfrage Neuware - Printed after ordering - 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.

Codice articolo 9781441928412

Dati bibliografici

• Titolo: Hidden Markov Models : Estimation and Control
• Editore: Springer New York, Springer US
• Data di pubblicazione: 2010
• Lingua: Inglese
• ISBN 10: 1441928413
• ISBN 13: 9781441928412
• Rilegatura: Taschenbuch
• Condizione: Neu
• Dimensioni: 234 L x 156 A x 22 P (millimetri) 234 millimetri di larghezza per 156 millimetri di altezza per 22 millimetri di profondità
• Peso: 600 grammi

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Riassunto

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.