Olivier Cappé,
Eric Moulines,
Tobias Ryden
Springer New York
1e druk, 2007
9780387402642
Inference in Hidden Markov Models
Specificaties
Gebonden, 653 blz.
|
Engels
Springer New York |
1e druk, 2007
ISBN13: 9780387402642
Rubricering
Onderdeel van serie
Springer Series in Statistics
Verwachte levertijd ongeveer 8 werkdagen
Samenvatting
This book is a comprehensive treatment of inference for hidden Markov models, including both algorithms and statistical theory. Topics range from filtering and smoothing of the hidden Markov chain to parameter estimation, Bayesian methods and estimation of the number of states. In a unified way the book covers both models with finite state spaces and models with continuous state spaces (also called state-space models) requiring approximate simulation-based algorithms that are also described in detail. Many examples illustrate the algorithms and theory. This book builds on recent developments to present a self-contained view.
Specificaties
ISBN13:9780387402642
Taal:Engels
Bindwijze:gebonden
Aantal pagina's:653
Uitgever:Springer New York
Druk:1
Inhoudsopgave
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.