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Inférence dans les modèles cachés de Markov 2005 cape mathématiques couverture rigide

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Caractéristiques de l'objet

État
Bon état: Livre ayant déjà été lu, mais qui est toujours en bon état. La couverture présente des ...
ISBN
9780387402642
Publication Year
2007
Series
Springer Series in Statistics Ser.
Type
Textbook
Format
Hardcover
Language
English
Publication Name
Inference in Hidden Markov Models
Author
Tobias Ryden, Eric Moulines, Olivier Cappé
Item Length
9.3in
Publisher
Springer New York
Item Width
6.1in
Item Weight
86.8 Oz
Number of Pages
Xvii, 653 Pages

À propos de ce produit

Product Information

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. 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. Many examples illustrate the algorithms and theory. The book builds on recent developments, both at the foundational level and the computational level, to present a self-contained view.

Product Identifiers

Publisher
Springer New York
ISBN-10
0387402640
ISBN-13
9780387402642
eBay Product ID (ePID)
46028072

Product Key Features

Author
Tobias Ryden, Eric Moulines, Olivier Cappé
Publication Name
Inference in Hidden Markov Models
Format
Hardcover
Language
English
Publication Year
2007
Series
Springer Series in Statistics Ser.
Type
Textbook
Number of Pages
Xvii, 653 Pages

Dimensions

Item Length
9.3in
Item Width
6.1in
Item Weight
86.8 Oz

Additional Product Features

Number of Volumes
1 Vol.
Lc Classification Number
Qa273.A1-274.9
Reviews
From the reviews:"By providing an overall survey of results obtained so far in a very readable manner, and also presenting some new ideas, this well-written book will appeal to academic researchers in the field of HMMs, with PhD students working on related topics included. It will also appeal to practitioners and researchers from other fields by guiding them through the computational steps needed for making inference HMMs and/or by providing them with the relevant underlying statistical theory. In the reviewer's opinion this book will shortly become a reference work in its field." MathSciNet"This monograph is a valuable resource. It provides a good literature review, an excellent account of the state of the art research on the necessary theory and algorithms, and ample illustrations of numerous applications of HMM. It goes much beyond the earlier resources on HMM...I anticipate this work to serve well many Technometrics readers in the coming years." Haikady N. Nagaraja for Technometrics, November 2006"This monograph is an attempt to present a reasonably complete up-to-date picture of the field of Hidden Markov Models (HMM) that is self-contained from a theoretical point of view and self sufficient from a methodological point of view. … The book is written for academic researchers in the field of HMMs, and also for practitioners and researchers from other fields. … all the theory is illustrated with relevant running examples. This voluminous book has indeed the potential to become a standard text on HMM." (R. Schlittgen, Zentralblatt MATH, Vol. 1080, 2006)"Providing an overall survey of results obtained so far in a very readable manner … this well-written book will appeal to academic researchers in the field of HMMs, with PhD students working on related topics included. It will also appeal to practitioners and researchers from other fields by guiding them through the computational steps needed for making interference on HMMs and/or by providing them with the relevant underlying statistical theory. In the reviewer's opinion this book will shortly become a reference work in its field." (M. Iosifescu, Mathematical Reviews, Issue 2006 e)"The authors describe Hidden Markov Models (HMMs) as 'one of the most successful statistical modelling ideas … in the last forty years.' The book considers both finite and infinite sample spaces. … Illustrative examples … recur throughout the book. … This fascinating book offers new insights into the theory and application of HMMs, and in addition it is a useful source of reference for the wide range of topics considered." (B. J. T. Morgan, Short Book Reviews, Vol. 26 (2), 2006)"In Inference in Hidden Markov Models, Cappé et al. present the current state of the art in HMMs in an emminently readable, thorough, and useful way. This is a very well-written book … . The writing is clear and concise. … the book will appeal to academic researchers in the field of HMMs, in particular PhD students working on related topics, by summing up the results obtained so far and presenting some new ideas … ." (Robert Shearer, Interfaces, Vol. 37 (2), 2007), "By providing an overall survey of results obtained so far in a very readable manner, and also presenting some new ideas, this well-written book will appeal to academic researchers in the field of HMMs, with PhD students working on related topics included. It will also appeal to practitioners and researchers from other fields by guiding them through the computational steps needed for making inference HMMs and/or by providing them with the relevant underlying statistical theory. In the reviewer's opinion this book will shortly become a reference work in its field." MathSciNet, From the reviews: "By providing an overall survey of results obtained so far in a very readable manner, and also presenting some new ideas, this well-written book will appeal to academic researchers in the field of HMMs, with PhD students working on related topics included. It will also appeal to practitioners and researchers from other fields by guiding them through the computational steps needed for making inference HMMs and/or by providing them with the relevant underlying statistical theory. In the reviewer's opinion this book will shortly become a reference work in its field." MathSciNet "This monograph is a valuable resource. It provides a good literature review, an excellent account of the state of the art research on the necessary theory and algorithms, and ample illustrations of numerous applications of HMM. It goes much beyond the earlier resources on HMM...I anticipate this work to serve well many Technometrics readers in the coming years." Haikady N. Nagaraja for Technometrics, November 2006 "This monograph is an attempt to present a reasonably complete up-to-date picture of the field of Hidden Markov Models (HMM) that is self-contained from a theoretical point of view and self sufficient from a methodological point of view. ... The book is written for academic researchers in the field of HMMs, and also for practitioners and researchers from other fields. ... all the theory is illustrated with relevant running examples. This voluminous book has indeed the potential to become a standard text on HMM." (R. Schlittgen, Zentralblatt MATH, Vol. 1080, 2006) "Providing an overall survey of results obtained so far in a very readable manner ... this well-written book will appeal to academic researchers in the field of HMMs, with PhD students working on related topics included. It will also appeal to practitioners and researchers from other fields by guiding them through the computational steps needed for making interference on HMMs and/or by providing them with the relevant underlying statistical theory. In the reviewer's opinion this book will shortly become a reference work in its field." (M. Iosifescu, Mathematical Reviews, Issue 2006 e) "The authors describe Hidden Markov Models (HMMs) as 'one of the most successful statistical modelling ideas ... in the last forty years.' The book considers both finite and infinite sample spaces. ... Illustrative examples ... recur throughout the book. ... This fascinating book offers new insights into the theory and application of HMMs, and in addition it is a useful source of reference for the wide range of topics considered." (B. J. T. Morgan, Short Book Reviews, Vol. 26 (2), 2006) "In Inference in Hidden Markov Models, Cappé et al. present the current state of the art in HMMs in an emminently readable, thorough, and useful way. This is a very well-written book ... . The writing is clear and concise. ... the book will appeal to academic researchers in the field of HMMs, in particular PhD students working on related topics, by summing up the results obtained so far and presenting some new ideas ... ." (Robert Shearer, Interfaces, Vol. 37 (2), 2007), From the reviews: "By providing an overall survey of results obtained so far in a very readable manner, and also presenting some new ideas, this well-written book will appeal to academic researchers in the field of HMMs, with PhD students working on related topics included. It will also appeal to practitioners and researchers from other fields by guiding them through the computational steps needed for making inference HMMs and/or by providing them with the relevant underlying statistical theory. In the reviewer's opinion this book will shortly become a reference work in its field." MathSciNet "This monograph is a valuable resource. It provides a good literature review, an excellent account of the state of the art research on the necessary theory and algorithms, and ample illustrations of numerous applications of HMM. It goes much beyond the earlier resources on HMM...I anticipate this work to serve well many Technometrics readers in the coming years." Haikady N. Nagaraja for Technometrics, November 2006 "This monograph is an attempt to present a reasonably complete up-to-date picture of the field of Hidden Markov Models (HMM) that is self-contained from a theoretical point of view and self sufficient from a methodological point of view. ... The book is written for academic researchers in the field of HMMs, and also for practitioners and researchers from other fields. ... all the theory is illustrated with relevant running examples. This voluminous book has indeed the potential to become a standard text on HMM." (R. Schlittgen, Zentralblatt MATH, Vol. 1080, 2006) "Providing an overall survey of results obtained so far in a very readable manner ... this well-written book will appeal to academic researchers in the field of HMMs, with PhD students working on related topics included. It will also appeal to practitioners and researchers from other fields by guiding them through the computational steps neededfor making interference on HMMs and/or by providing them with the relevant underlying statistical theory. In the reviewer's opinion this book will shortly become a reference work in its field." (M. Iosifescu, Mathematical Reviews, Issue 2006 e) "The authors describe Hidden Markov Models (HMMs) as 'one of the most successful statistical modelling ideas ... in the last forty years.' The book considers both finite and infinite sample spaces. ... Illustrative examples ... recur throughout the book. ... This fascinating book offers new insights into the theory and application of HMMs, and in addition it is a useful source of reference for the wide range of topics considered." (B. J. T. Morgan, Short Book Reviews, Vol. 26 (2), 2006) "In Inference in Hidden Markov Models, Cappé et al. present the current state of the art in HMMs in an emminently readable, thorough, and useful way. This is a very well-written book ... . The writing is clear and concise. ... the book will appeal to academic researchers in the field of HMMs, in particular PhD students working on related topics, by summing up the results obtained so far and presenting some new ideas ... ." (Robert Shearer, Interfaces, Vol. 37 (2), 2007)
Table of Content
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.
Copyright Date
2005
Topic
Signals & Signal Processing, Probability & Statistics / General, Statistics
Lccn
2005-923551
Dewey Decimal
519.233
Intended Audience
Scholarly & Professional
Dewey Edition
22
Illustrated
Yes
Genre
Technology & Engineering, Business & Economics, Mathematics

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