Inference in Hidden 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 statist...
Shranjeno v:
| Auteurs principaux: | , , |
|---|---|
| Format: | Livre numérique |
| Jezik: | Anglais |
| Izdano: |
New York, NY :
Springer New York
[20..].
Cham : Springer Nature |
| Izdaja: | 1st ed. 2005. |
| Serija: | Springer Series in Statistics
|
| Teme: | |
| Online dostop: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Sporočilo: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Inference in hidden Markov models, Olivier Cappé, Eric Moulines, Tobias Rydén, 2005, [New York], Springer, 1 volume (XVII-652 p.), Springer series in statistics, 978-0-387-40264-2 |
Kazalo:
- 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.
- 5, Main Definitions and Notations
- 6, State Inference
- 7, Parameter Inference
- 8, Background and Complements
- An Information-Theoretic Perspective on Order Estimation
- 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
- 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
- Elements of Markov Chain Theory