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Explicit-Duration Markov Switching Models: 24 (Foundations and Trends® in Machine Learning) - Tapa blanda

Chiappa, Silvia

 
9781601988300: Explicit-Duration Markov Switching Models: 24 (Foundations and Trends® in Machine Learning)
Tapa blanda
ISBN 10: 1601988303 ISBN 13: 9781601988300
Editorial: Now Publishers Inc, 2014

Sinopsis

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 differin 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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Reseña del editor

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.

Reseña del editor

Time-series are often modelled probabilistically as sequences of segments using first-order Markovian hidden variables representing different dynamics regimes. Such dynamics-regime variables implicitly define a geometric segment-duration distribution. A more flexible distribution requires introducing extra variables into the model with increased computational cost. This can be achieved in different ways, whose properties and categorization are still unclear. This book describes in a simple way different approaches to modelling the segment-duration distribution by categorizing them into three main groups, differing respectively by explicitly encoding information about segment beginning, end and both beginning and end by means of different segment-duration variables and associated distributions. The approaches are described using the framework of belief networks, which makes it possible to visually and therefore easily describe model structure and assess independence among random variables. This description enables a simple and clear understanding of approaches and inference routines in the literature but also to gain new insights especially about models with complex hidden variables.

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Editorial
Now Publishers Inc
Año de publicación
2014
Idioma
Inglés
ISBN 10 
1601988303
ISBN 13 
9781601988300
Encuadernación
Tapa blanda
Número de páginas
102
Contacto del fabricante
no disponible
Persona responsable
no disponible