Elliott / Aggoun / Moore Hidden Markov Models

Estimation and Control
1. Auflage 1995
ISBN: 978-0-387-84854-9
Verlag: Springer US
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 29, 382 Seiten, eBook

Reihe: Stochastic Modelling and Applied Probability

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.

Zielgruppe

Research

Autoren/Hrsg.

Elliott, Robert J

Aggoun, Lakhdar

Moore, John B.

Inhaltsverzeichnis

Hidden Markov Model Processing.- Discrete-Time HMM Estimation.- Discrete States and Discrete Observations.- Continuous-Range Observations.- Continuous-Range States and Observations.- A General Recursive Filter.- Practical Recursive Filters.- Continuous-Time HMM Estimation.- Discrete-Range States and Observations.- Markov Chains in Brownian Motion.- Two-Dimensional HMM Estimation.- Hidden Markov Random Fields.- HMM Optimal Control.- Discrete-Time HMM Control.- Risk-Sensitive Control of HMM.- Continuous-Time HMM Control.