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Introduction to hidden semi-Markov models/ Hoek, John van der & Elliot, Robert J

By: Contributor(s): Series: London Mathematical Society Lecture Note Series ; 445Publication details: UK: CUP, 2018Description: x, 174 pages, 23 cmISBN:
  • 9781108441988
Subject(s): DDC classification:
  • 23 519.233 H693
Contents:
Observed Markov Chains -- Estimation of an observed Markov chain -- Hidden Markov models -- Filters and smoothers -- The Viterbi algorithm -- The EM algorithm -- A New Markov chain model -- Semi-Markov models -- Hidden semi-Markov models -- Filters for hidden semi-Markov models
Summary: 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 This book contains new results and previously unpublished material not available elsewhere.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.
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Holdings
Item type Current library Call number Status Date due Barcode Item holds
Books ISI Library, Kolkata NBHM Collection 519.233 H693 (Browse shelf(Opens below)) Available 138633
Total holds: 0

ncludes bibliography and index

Observed Markov Chains -- Estimation of an observed Markov chain -- Hidden Markov models -- Filters and smoothers -- The Viterbi algorithm -- The EM algorithm -- A New Markov chain model -- Semi-Markov models -- Hidden semi-Markov models -- Filters for hidden semi-Markov models

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.

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