Inference in Hiddeen Markov Models

"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 s...

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Bibliografische gegevens
Hoofdauteur: Olivier Cappé
Andere auteurs: Eric Moulines; Tobias Rydén
Formaat: Printed Book
Gepubliceerd in: Springer 2005
Reeks:Springer series in statistics.
Onderwerpen:
Markov processes.
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100 |a  Olivier Cappé; 
245 |a Inference in Hiddeen Markov Models 
260 |b Springer  |c 2005 
300 |a  xvii, 652 pages : illustrations ; 25 cm. 
490 |a  Springer series in statistics. 
505 |a 1. Introduction -- 2. Main definitions and notations -- pt. I. State inference -- 3. Filtering and smoothing recursions -- 4. Advanced topics in smoothing -- 5. Applications of smoothing -- 6. Monte Carlo methods -- 7. Sequential Monte Carlo methods -- 8. Advanced topics in sequential Monte Carlo -- 9. Analysis of sequential Monte Carlo methods -- pt. II. Parameter inference -- 10. Maximum likelihood inference, part I : optimization through exact smoothing -- 11. Maximum likelihood inference, part II : Monte Carlo optimization -- 12. Statistical properties of the maximum likelihood estimator -- 13. Fully Bayesian approaches -- pt. III. Background and complements -- 14. Elements of Markov chain theory -- 15. An information-theoretic perspective on order estimation -- App. A. Conditioning -- App. B. Linear prediction -- App. C. Notations. 
520 |a "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." "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."--Jacket 
650 |a  Markov processes.  
700 |a Eric Moulines; Tobias Rydén 
942 |c BK 
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