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Synopsis
Explicit-Duration Markov Switching Models provides a simple and clear description of explicit-duration modeling by categorizing the different approaches into three main groups, which differ in encoding in the explicit-duration variables different information about regime switching/reset boundaries. The approaches are described using the formalism of graphical models, which enables graphical representation and assessment of statistical dependence, and therefore makes it easy to describe the structure of complex models and derive inference routines. The presentation is pedagogical, focusing on making distinctions that help structure the space of models and in laying out inference and learning in a clear way. Explicit-Duration Markov Switching Models is an ideal reference for students and researchers who wish to learn about these models and those looking to develop them further.
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About the Author
Silvia Chiappa is a Marie Curie Fellow at the Statistical Laboratory, Cambridge.
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A Simple Tutorial on Hidden Markov Switching Models With Explicit Segment-Duration Distribution (Foundations and Trends in Machine Learning)
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Explicit-Duration Markov Switching Models (Foundations and Trends (R) in Machine Learning): 24 (Foundations and Trends® in Machine Learning) [Paperback] Chiappa, Silvia
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A Simple Tutorial on Hidden Markov Switching Models With Explicit Segment-Duration Distribution (Foundations and Trends in Machine Learning)
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A Simple Tutorial on Hidden Markov Switching Models With Explicit Segment-Duration Distribution (Foundations and Trends in Machine Learning)
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