Hidden Semi-Markov Models

Informationen zum Autor Shun-Zheng Yu is a professor at the School of Information Science and Technology at Sun Yat-Sen University, China.. He was a visiting scholar at Princeton University and IBM Thomas J. Watson Research Center from 1999 to 2002. He has authored two hundred journal papers that used artificial intelligence/machine learning methods for inference and estimation, among which fifty papers involved hidden semi-Markov models. Professor Yu is a well-recognized expert in the field of HSMMs and their applications. He has developed new estimation algorithms for HSMMs and applied them in various fields. The papers entitled "Hidden Semi-Markov Models (2010)" Published in the Elsevier Journal Artificial Intelligence , "Practical Implementation of an Efficient Forward-Backward Algorithm for an Explicit Duration Hidden Markov Model (2006) published in IEEE Signal Processing Letters", "A Hidden Semi-Markov Model with Missing Data and Multiple Observation Sequences for Mobility Tracking (2003)" Published in the Elsevier Journal Signal Processing and " An Efficient Forward-Backward Algorithm for an Explicit Duration Hidden Markov Model (2003) published in IEEE Signal Processing Letters " have been cited by hundreds of papers. Klappentext Hidden Semi-Markov Models: Theory! Algorithms and Applications provides a unified and foundational approach on Hidden Semi-Markov Models! including various HSMMs (such as the explicit duration! variable transition! and residential time of HSMMs)! inference and estimation algorithms! implementation methods and application instances. . In addition! new developments and state-of-the-art emerging topics as they relate to HSMMs are presented with general examples drawn medicine! engineering and computer science.

Autorentext
Shun-Zheng Yu is a professor at the School of Information Science and Technology at Sun Yat-Sen University, China.. He was a visiting scholar at Princeton University and IBM Thomas J. Watson Research Center from 1999 to 2002. He has authored two hundred journal papers that used artificial intelligence/machine learning methods for inference and estimation, among which fifty papers involved hidden semi-Markov models. Professor Yu is a well-recognized expert in the field of HSMMs and their applications. He has developed new estimation algorithms for HSMMs and applied them in various fields. The papers entitled "Hidden Semi-Markov Models (2010)" Published in the Elsevier Journal Artificial Intelligence , "Practical Implementation of an Efficient Forward-Backward Algorithm for an Explicit Duration Hidden Markov Model (2006) published in IEEE Signal Processing Letters", "A Hidden Semi-Markov Model with Missing Data and Multiple Observation Sequences for Mobility Tracking (2003)" Published in the Elsevier Journal Signal Processing and " An Efficient Forward-Backward Algorithm for an Explicit Duration Hidden Markov Model (2003) published in IEEE Signal Processing Letters " have been cited by hundreds of papers.

Klappentext

Hidden Semi-Markov Models: Theory, Algorithms and Applications provides a unified and foundational approach on Hidden Semi-Markov Models, including various HSMMs (such as the explicit duration, variable transition, and residential time of HSMMs), inference and estimation algorithms, implementation methods and application instances. . In addition, new developments and state-of-the-art emerging topics as they relate to HSMMs are presented with general examples drawn medicine, engineering and computer science.

Zusammenfassung
"This book is intended to present theory, models, methods, and applications regarding hidden semi-Markov models...It also provides the latest development and emerging topics concerning this field." --Zentralblatt MATH

Inhalt

1. Introduction2. Inference of General Hidden Semi-Markov Model3. Estimation of General Hidden Semi-Markov Model4. Implementation of the Algorithms5. Conventional Models6. Various Duration Distributions8. Variants of HSMM9. Applications of HSMM