The Resource Inference in Hidden Markov Models, by Olivier Cappé, Eric Moulines, Tobias Ryden, (electronic resource)

Inference in Hidden Markov Models, by Olivier Cappé, Eric Moulines, Tobias Ryden, (electronic resource)

Label
Inference in Hidden Markov Models
Title
Inference in Hidden Markov Models
Statement of responsibility
by Olivier Cappé, Eric Moulines, Tobias Ryden
Creator
Contributor
Author
Author
Subject
Language
  • eng
  • eng
Summary
Hidden Markov models have become a widely used class of statistical models with applications in diverse areas such as communications engineering, bioinformatics, finance and many more. This book is a comprehensive treatment of inference for hidden Markov models, including both algorithms and statistical theory. Topics range from filtering and smoothing of the hidden Markov chain to parameter estimation, Bayesian methods and estimation of the number of states. In a unified way the book covers both models with finite state spaces, which allow for exact algorithms for filtering, estimation etc. and models with continuous state spaces (also called state-space models) requiring approximate simulation-based algorithms that are also described in detail. Simulation in hidden Markov models is addressed in five different chapters that cover both Markov chain Monte Carlo and sequential Monte Carlo approaches. Many examples illustrate the algorithms and theory. The book also carefully treats Gaussian linear state-space models and their extensions and it contains a chapter on general Markov chain theory and probabilistic aspects of hidden Markov models. This volume will suit anybody with an interest in inference for stochastic processes, and it will be useful for researchers and practitioners in areas such as statistics, signal processing, communications engineering, control theory, econometrics, finance and more. The algorithmic parts of the book do not require an advanced mathematical background, while the more theoretical parts require knowledge of probability theory at the measure-theoretical level. Olivier Cappé is Researcher for the French National Center for Scientific Research (CNRS). He received the Ph.D. degree in 1993 from Ecole Nationale Supérieure des Télécommunications, Paris, France, where he is currently a Research Associate. Most of his current research concerns computational statistics and statistical learning. Eric Moulines is Professor at Ecole Nationale Supérieure des Télécommunications (ENST), Paris, France. He graduated from Ecole Polytechnique, France, in 1984 and received the Ph.D. degree from ENST in 1990. He has authored more than 150 papers in applied probability, mathematical statistics and signal processing. Tobias Rydén is Professor of Mathematical Statistics at Lund University, Sweden, where he also received his Ph.D. in 1993. His publications include papers ranging from statistical theory to algorithmic developments for hidden Markov models
Member of
Is Subseries of
http://library.link/vocab/creatorName
Cappé, Olivier
Dewey number
519.233
http://bibfra.me/vocab/relation/httpidlocgovvocabularyrelatorsaut
  • kvPa56iHHes
  • lCcWk21ZXlU
  • z4PKPP0fCzI
Language note
English
LC call number
  • QA273.A1-274.9
  • QA274-274.9
Literary form
non fiction
Nature of contents
dictionaries
http://library.link/vocab/relatedWorkOrContributorName
  • Moulines, Eric.
  • Ryden, Tobias.
Series statement
Springer Series in Statistics,
http://library.link/vocab/subjectName
  • Distribution (Probability theory
  • Mathematical statistics
  • Statistics
  • Computer simulation
  • Probability Theory and Stochastic Processes
  • Statistical Theory and Methods
  • Signal, Image and Speech Processing
  • Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences
  • Statistics for Business, Management, Economics, Finance, Insurance
  • Simulation and Modeling
Label
Inference in Hidden Markov Models, by Olivier Cappé, Eric Moulines, Tobias Ryden, (electronic resource)
Instantiates
Publication
Note
Description based upon print version of record
Bibliography note
Includes bibliographical references and index
Carrier category
online resource
Carrier category code
cr
Content category
text
Content type code
txt
Contents
Main Definitions and Notations -- Main Definitions and Notations -- State Inference -- Filtering and Smoothing Recursions -- Advanced Topics in Smoothing -- Applications of Smoothing -- Monte Carlo Methods -- Sequential Monte Carlo Methods -- Advanced Topics in Sequential Monte Carlo -- Analysis of Sequential Monte Carlo Methods -- Parameter Inference -- Maximum Likelihood Inference, Part I: Optimization Through Exact Smoothing -- Maximum Likelihood Inference, Part II: Monte Carlo Optimization -- Statistical Properties of the Maximum Likelihood Estimator -- Fully Bayesian Approaches -- Background and Complements -- Elements of Markov Chain Theory -- An Information-Theoretic Perspective on Order Estimation
Dimensions
unknown
Edition
1st ed. 2005.
Extent
1 online resource (667 p.)
Form of item
online
Isbn
9786611114329
Media category
computer
Media type code
c
Other control number
10.1007/0-387-28982-8
Specific material designation
remote
System control number
  • (CKB)1000000000228073
  • (EBL)264849
  • (OCoLC)262680053
  • (SSID)ssj0000178818
  • (PQKBManifestationID)11183082
  • (PQKBTitleCode)TC0000178818
  • (PQKBWorkID)10229878
  • (PQKB)10757600
  • (SSID)ssj0000770931
  • (PQKBManifestationID)12318158
  • (PQKBTitleCode)TC0000770931
  • (PQKBWorkID)10790296
  • (PQKB)11124838
  • (DE-He213)978-0-387-28982-3
  • (MiAaPQ)EBC264849
  • (EXLCZ)991000000000228073
Label
Inference in Hidden Markov Models, by Olivier Cappé, Eric Moulines, Tobias Ryden, (electronic resource)
Publication
Note
Description based upon print version of record
Bibliography note
Includes bibliographical references and index
Carrier category
online resource
Carrier category code
cr
Content category
text
Content type code
txt
Contents
Main Definitions and Notations -- Main Definitions and Notations -- State Inference -- Filtering and Smoothing Recursions -- Advanced Topics in Smoothing -- Applications of Smoothing -- Monte Carlo Methods -- Sequential Monte Carlo Methods -- Advanced Topics in Sequential Monte Carlo -- Analysis of Sequential Monte Carlo Methods -- Parameter Inference -- Maximum Likelihood Inference, Part I: Optimization Through Exact Smoothing -- Maximum Likelihood Inference, Part II: Monte Carlo Optimization -- Statistical Properties of the Maximum Likelihood Estimator -- Fully Bayesian Approaches -- Background and Complements -- Elements of Markov Chain Theory -- An Information-Theoretic Perspective on Order Estimation
Dimensions
unknown
Edition
1st ed. 2005.
Extent
1 online resource (667 p.)
Form of item
online
Isbn
9786611114329
Media category
computer
Media type code
c
Other control number
10.1007/0-387-28982-8
Specific material designation
remote
System control number
  • (CKB)1000000000228073
  • (EBL)264849
  • (OCoLC)262680053
  • (SSID)ssj0000178818
  • (PQKBManifestationID)11183082
  • (PQKBTitleCode)TC0000178818
  • (PQKBWorkID)10229878
  • (PQKB)10757600
  • (SSID)ssj0000770931
  • (PQKBManifestationID)12318158
  • (PQKBTitleCode)TC0000770931
  • (PQKBWorkID)10790296
  • (PQKB)11124838
  • (DE-He213)978-0-387-28982-3
  • (MiAaPQ)EBC264849
  • (EXLCZ)991000000000228073

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