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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. 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
- Sales Rank: #2555414 in eBooks
- Published on: 2006-04-18
- Released on: 2006-04-18
- Format: Kindle eBook
Review
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 Back Cover
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.
Most helpful customer reviews
3 of 3 people found the following review helpful.
formal but thorough
By Joshua V. Dillon
I don't usually leave reviews but I want to say that this is a very good book but indeed it is formal and can be overly-burdened with notation. If you are not comfortable with measure theory then you will likely find the presentation overly complicated (although, from a formal perspective it is certainly not). The authors present self-contained proofs which more than makes-up for the time spent decoding the notation (which differs only cosmetic from EE/machine learning presentations). To my knowledge, this is the only textbook treatment of both statistical and algorithmic convergence for several different latent variable inference algorithms. My reading was limited to the Stochastic EM sections (EM,MCEM,Robbins-Monroe; chapters 11 & 12). This is not my favorite textbook, but I do consider it a hidden gem.
21 of 25 people found the following review helpful.
not well organized
By jim
This book is more about measure theory and pure statistical properties. For me (as a practicer), I found it's difficult to
extract important information for the book.
My biggest complain is in first few chapters, the authers simply
list all definition, properties and proofs without a single example
to help readers understand. And the notations are quite complicated which gives readers no clue if one reads directly from the later chapters (more algorithms involved).
Through out the entire book, I can't find any complete and concrete
applications. The bottomline is this book is neither practical
nor can serve a textbook to understand fundamental statistical theories.
8 of 9 people found the following review helpful.
Comprehensive and detailed
By Reader
I am a Ph.D. student in Statistics and recently bought this book for background reading related to my research in credit risk modeling with latent variables. The authors were able to produce a very readable treatment of Markov Chains, Monte Carlo methods and the EM algorithm. The book contains plenty of examples in finance, communications theory, ... Overall, I am very pleased with my decision to buy this book.
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