ENGLISH

Hidden Markov models: estimation and control

By Robert J. Elliott , John B. Moore , Lakhdar Aggoun (auth.)

Book information

Publisher
Springer-Verlag New York
Year
1995
ISBN
0387943641, 9780387943640
DOI
10.1007/978-0-387-84854-9
LCC
QA274.7 .E45 1995
Open Library ID
OL1103619M
Language
english
Format
PDF
Filesize
5 MB (5614502 bytes)
Series
Stochastic Modelling and Applied Probability 29
Edition
1
Pages
382\374
Scanned
no
Time added
2009-08-06 05:14:26
Control; Probability Theory and Stochastic ProcessSystems Theory

Description

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.

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