Hidden Semi-Markov models : theory, algorithms and applications /

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Bibliographic Details
Author / Creator:Yu, Shun-Zheng, author.
Imprint:Amsterdam, Netherlands : Elsevier, [2016]
Description:1 online resource : illustrations.
Series:Computer science reviews and trends
Computer science reviews and trends.
Subject:Markov processes.
Renewal theory.
MATHEMATICS / Probability & Statistics / General
Markov processes.
Renewal theory.
Electronic books.
Format: E-Resource Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/11249142
Hidden Bibliographic Details
Notes:Includes bibliographical references.
Online resource; title from PDF title page (EBSCO, viewed October 29, 2015).
Summary:Hidden semi-Markov models (HSMMs) are among the most important models in the area of artificial intelligence / machine learning. Since the first HSMM was introduced in 1980 for machine recognition of speech, three other HSMMs have been proposed, with various definitions of duration and observation distributions. Those models have different expressions, algorithms, computational complexities, and applicable areas, without explicitly interchangeable forms. Hidden Semi-Markov Models: Theory, Algorithms and Applications provides a unified and foundational approach to HSMMs, including various HSMMs (such as the explicit duration, variable transition, and residential time of HSMMs), inference and estimation algorithms, implementation methods and application instances. Learn new developments and state-of-the-art emerging topics as they relate to HSMMs, presented with examples drawn from medicine, engineering and computer science.
Other form:Print version: Yu, Shun-Zheng. Hidden semi-markov models : theory, algorithms and applications. Amsterdam, [Netherlands] : Elsevier, c2016 ix, 195 pages Computer science reviews and trends. 9780128027677