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...
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| Autor Corporativo: | |
| Otros Autores: | , |
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
New York, NY :
Springer New York
2005.
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| Colección: | Springer series in statistics.
Springer eBooks. |
| Acceso en línea: | Conectar con la versión electrónica |
| Ver en Universidad de Navarra: | https://innopac.unav.es/record=b32735832*spi |
Tabla de Contenidos:
- 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.
