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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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| Format: | Printed Book |
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Springer
2005
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| Serija: | Springer series in statistics.
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Kazalo:
- 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.