Ex-library with stamp and library-signature. GOOD condition, some traces of use. Ancien Exemplaire de biblioth�que avec signature et cachet. BON �tat, quelques traces d'usure. Ehem. Bibliotheksexemplar mit Signatur und Stempel. GUTER Zustand, ein paar Gebrauchsspuren. 60 ELL 9780387943640 Sprache: Englisch Gewicht in Gramm: 1150.

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Detalles bibliogr�ficos

T�tulo

Hidden Markov models estimation and control Robert J. Elliott, Lakhdar Aggoun, John B. Moore. Applications of Mathematics 29; Applications of mathematics 29.

Editor

New York, Springer [1995].

Fecha de publicaci�n

1995

Idioma

Ingl�s

ISBN 10

0387943641

ISBN 13

9780387943640

Encuadernaci�n

Hardcover

Edici�n

3� Edici�n

Peso

1150 gramos

Cat�logos de vendedores

SA MATHEMATIK

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Sinopsis

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.

De la contraportada

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.

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