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  3. Introduction to Hidden Semi-Markov Models
  • Cited by 8
Publisher:
Cambridge University Press
Online publication date:
February 2018
Print publication year:
2018
Online ISBN:
9781108377423
DOI:
https://doi.org/10.1017/9781108377423
Subjects:
Mathematics (general), Genomics, Bioinformatics and Systems Biology, Life Sciences, Applied Probability and Stochastic Networks, Statistics and Probability
Series:
London Mathematical Society Lecture Note Series (445)
Information

Book description

Markov chains and hidden Markov chains have applications in many areas of engineering and genomics. This book provides a basic introduction to the subject by first developing the theory of Markov processes in an elementary discrete time, finite state framework suitable for senior undergraduates and graduates. The authors then introduce semi-Markov chains and hidden semi-Markov chains, before developing related estimation and filtering results. Genomics applications are modelled by discrete observations of these hidden semi-Markov chains. This book contains new results and previously unpublished material not available elsewhere. The approach is rigorous and focused on applications.

Reviews

'… this book is of interest to researchers attracted by hidden Markov and semi-Markov models. It covers probabilistic and statistical treatments of the considered topics, and introduces the reader … to possible applications, mainly in genomics. Hence, Ph.D. students and specialists in the area of hidden Markov processes are invited to consider this book as a reference in their activities.'

Antonio Di Crescenzo Source: MathSciNet

‘… dedicated mostly to graduate students and providing a rigorous and rather complete mathematical introduction to the theory of hidden Markov models as well as hidden semi-Markov models under main assumption that the hidden process is a finite state Markov chain. The semi-Markov models appear when the assumption that the length of time the chain spends in any state is geometrically distributed is relaxed. The authors carefully construct these processes on the canonical probability space and then derive filters and smoother, as well as the Viterbi estimates. The central role plays the EM Algorithm.’

Jerzy Ombach Source: ZB Math Reviews

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Contents

Contents
References
References
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