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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)
Resource Information
The item Inference in Hidden Markov Models, by Olivier Cappé, Eric Moulines, Tobias Ryden, (electronic resource) represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in University of Oklahoma Libraries.This item is available to borrow from all library branches.
Resource Information
The item Inference in Hidden Markov Models, by Olivier Cappé, Eric Moulines, Tobias Ryden, (electronic resource) represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in University of Oklahoma Libraries.
This item is available to borrow from all library branches.
 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 statespace models) requiring approximate simulationbased 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 statespace 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 measuretheoretical 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
 Language

 eng
 eng
 Edition
 1st ed. 2005.
 Extent
 1 online resource (667 p.)
 Note
 Description based upon print version of record
 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 InformationTheoretic Perspective on Order Estimation
 Isbn
 9786611114329
 Label
 Inference in Hidden Markov Models
 Title
 Inference in Hidden Markov Models
 Statement of responsibility
 by Olivier Cappé, Eric Moulines, Tobias Ryden
 Subject

 Statistics for Business, Management, Economics, Finance, Insurance
 Statistics
 Signal, Image and Speech Processing
 Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences
 Mathematical statistics
 Distribution (Probability theory
 Probability Theory and Stochastic Processes
 Statistical Theory and Methods
 Computer simulation
 Simulation and Modeling
 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 statespace models) requiring approximate simulationbased 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 statespace 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 measuretheoretical 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
 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.A1274.9
 QA274274.9
 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)
 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 InformationTheoretic 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
 10.1007/0387289828
 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
 (DEHe213)9780387289823
 (MiAaPQ)EBC264849
 (EXLCZ)991000000000228073
 Label
 Inference in Hidden Markov Models, by Olivier Cappé, Eric Moulines, Tobias Ryden, (electronic resource)
 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 InformationTheoretic 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
 10.1007/0387289828
 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
 (DEHe213)9780387289823
 (MiAaPQ)EBC264849
 (EXLCZ)991000000000228073
Subject
 Computer simulation
 Distribution (Probability theory
 Mathematical statistics
 Probability Theory and Stochastic Processes
 Signal, Image and Speech Processing
 Simulation and Modeling
 Statistical Theory and Methods
 Statistics
 Statistics for Business, Management, Economics, Finance, Insurance
 Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences
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<div class="citation" vocab="http://schema.org/"><i class="fa faexternallinksquare fafw"></i> Data from <span resource="http://link.libraries.ou.edu/portal/InferenceinHiddenMarkovModelsbyOlivier/xDojUSmwdI8/" typeof="Book http://bibfra.me/vocab/lite/Item"><span property="name http://bibfra.me/vocab/lite/label"><a href="http://link.libraries.ou.edu/portal/InferenceinHiddenMarkovModelsbyOlivier/xDojUSmwdI8/">Inference in Hidden Markov Models, by Olivier Cappé, Eric Moulines, Tobias Ryden, (electronic resource)</a></span>  <span property="potentialAction" typeOf="OrganizeAction"><span property="agent" typeof="LibrarySystem http://library.link/vocab/LibrarySystem" resource="http://link.libraries.ou.edu/"><span property="name http://bibfra.me/vocab/lite/label"><a property="url" href="http://link.libraries.ou.edu/">University of Oklahoma Libraries</a></span></span></span></span></div>