Inference in Hidden Markov Models
1st ed. 2005. Corr. 2nd printing 2007
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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.
Get Inference in Hidden Markov Models by Eric Moulines, Olivier Cappé at the best price and quality guaranteed only at Werezi Africa's largest book ecommerce store. The book was published by Springer-Verlag New York Inc. and it has 653 pages.
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