Hidden Semi-Markov models : theory, algorithms and applications / [electronic resource] Shun-Zheng Yu. Yu, Shun-Zheng, author. Markov processes. Renewal theory. QA274.7 519.2/33 23 Includes bibliographical references. Online resource; title from PDF title page (EBSCO, viewed October 29, 2015). 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. 2016 Text 1 online resource : http://www.sciencedirect.com/science/book/9780128027677 eng Computer science reviews and trends Computer science reviews and trends. Hidden semi-markov models : theory, algorithms and applications. Hidden semi-markov models : theory, algorithms and applications.