Inference in Hidden Markov Models

Authors:
Olivier Capp;Eric Moulines;Tobias Ryden
Affiliations:
-;-;-
Venue:
Inference in Hidden Markov Models
Year:
2010

Citing 0
Cited 23

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Fast estimation of posterior probabilities in change-point analysis through a constrained hidden Markov model

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Modelling species abundance in a river by Negative Binomial hidden Markov models

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Abstract

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.