Inference in Hidden Markov Models

• Main Authors: Cappé, Olivier (Author), Moulines, Eric (Author), Rydén, Tobias (Author)
• Corporate Author: SpringerLink (Online service)
• Format: Electronic eBook
• Language: English
• Published: New York, NY : Springer New York, 2005.
• Series: Springer Series in Statistics,
• Subjects: Mathematics.; Computer simulation.; Probabilities.; Statistics.; Probability Theory and Stochastic Processes.; Statistical Theory and Methods.; Signal, Image and Speech Processing.; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.; Statistics for Business/Economics/Mathematical Finance/Insurance.; Simulation and Modeling.

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...

Table of Contents:

• 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.