Hidden Markov models: estimation and control

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 r...

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Detalles Bibliográficos
Autores principales: Elliott, Robert J, Aggoun, Lakhdar, Moore, John B
Lenguaje:eng
Publicado: Springer 1995
Materias:
Mathematical Physics and Mathematics
Acceso en línea:https://dx.doi.org/10.1007/978-0-387-84854-9
http://cds.cern.ch/record/1601270
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author Elliott, Robert J
Aggoun, Lakhdar
Moore, John B
author_facet Elliott, Robert J
Aggoun, Lakhdar
Moore, John B
author_sort Elliott, Robert J
collection CERN
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 filte
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institution Organización Europea para la Investigación Nuclear
language eng
publishDate 1995
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spelling cern-16012702021-04-21T22:26:09Zdoi:10.1007/978-0-387-84854-9http://cds.cern.ch/record/1601270engElliott, Robert JAggoun, LakhdarMoore, John BHidden Markov models: estimation and controlMathematical Physics and MathematicsAs 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 filteSpringeroai:cds.cern.ch:16012701995
spellingShingle Mathematical Physics and Mathematics
Elliott, Robert J
Aggoun, Lakhdar
Moore, John B
Hidden Markov models: estimation and control
title Hidden Markov models: estimation and control
title_full Hidden Markov models: estimation and control
title_fullStr Hidden Markov models: estimation and control
title_full_unstemmed Hidden Markov models: estimation and control
title_short Hidden Markov models: estimation and control
title_sort hidden markov models: estimation and control
topic Mathematical Physics and Mathematics
url https://dx.doi.org/10.1007/978-0-387-84854-9
http://cds.cern.ch/record/1601270
work_keys_str_mv AT elliottrobertj hiddenmarkovmodelsestimationandcontrol
AT aggounlakhdar hiddenmarkovmodelsestimationandcontrol
AT moorejohnb hiddenmarkovmodelsestimationandcontrol

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