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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Dades bibliogràfiques
Autor principal: Olivier Cappé
Altres autors: Eric Moulines; Tobias Rydén
Format: Printed Book
Publicat: Springer 2005
Col·lecció:Springer series in statistics.
Matèries:
Markov processes.
Taula de continguts:
  • 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.

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