ENGLISH

Inference in Hidden Markov Models

By Olivier Cappé , Eric Moulines , Tobias Ryden

Book information

Publisher
Springer
Year
2005
ISBN
0387402640, 9780387402642, 9780387289823
LCC
QA274.7 .C375 2005
Google Books ID
-3_A3_l1yssC
Open Library ID
OL22634495M
Language
english
Format
PDF
Filesize
6 MB (6349600 bytes)
Series
Springer series in statistics
Edition
1st edition
Pages
652\652
Library
mexmat
Orientation
no
Scanned
no
Time added
2009-07-20 03:45:11
Математика;Теория вероятностей и математическая ст

Description

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 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, which allow for exact algorithms for filtering, estimation etc. and models with continuous state spaces (also called state-space models) requiring approximate simulation-based algorithms that are also described in detail. Simulation in hidden Markov models is addressed in five different chapters that cover both Markov chain Monte Carlo and sequential Monte Carlo approaches. Many examples illustrate the algorithms and theory. The book also carefully treats Gaussian linear state-space models and their extensions and it contains a chapter on general Markov chain theory and probabilistic aspects of hidden Markov models.

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