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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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Cappé, Olivier (Autor), Moulines, Eric (Autor), Ryden, Tobias (Autor)
Autor Corporativo: SpringerLink (Online service)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: New York, NY : Springer New York : Imprint: Springer, 2005.
Edición:1st ed. 2005.
Colección:Springer Series in Statistics,
Temas:
Probabilities.
Statistics .
Signal processing.
Computer simulation.
Probability Theory.
Statistical Theory and Methods.
Signal, Speech and Image Processing .
Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
Statistics in Business, Management, Economics, Finance, Insurance.
Computer Modelling.
Acceso en línea:Texto Completo
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.

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