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Enhanced Bayesian Network Models for Spatial Time Series Prediction: Recent Research Trend in Data-Driven Predictive Analytics: 858 (Studies in Computational Intelligence) - Tapa dura
Sinopsis
This research monograph is highly contextual in the present era of spatial/spatio-temporal data explosion. The overall text contains many interesting results that are worth applying in practice, while it is also a source of intriguing and motivating questions for advanced research on spatial data science.
The monograph is primarily prepared for graduate students of Computer Science, who wish to employ probabilistic graphical models, especially Bayesian networks (BNs), for applied research on spatial/spatio-temporal data. Students of any other discipline of engineering, science, and technology, will also find this monograph useful. Research students looking for a suitable problem for their MS or PhD thesis will also find this monograph beneficial. The open research problems as discussed with sufficient references in Chapter-8 and Chapter-9 can immensely help graduate researchers to identify topics of their own choice. The various illustrations and proofs presented throughout the monograph may help them to better understand the working principles of the models. The present monograph, containing sufficient description of the parameter learning and inference generation process for each enhanced BN model, can also serve as an algorithmic cookbook for the relevant system developers.
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Acerca del autor
Charlotte y Peter Fiell son dos autoridades en historia, teoría y crítica del diseño y han escrito más de sesenta libros sobre la materia, muchos de los cuales se han convertido en éxitos de ventas. También han impartido conferencias y cursos como profesores invitados, han comisariado exposiciones y asesorado a fabricantes, museos, salas de subastas y grandes coleccionistas privados de todo el mundo. Los Fiell han escrito numerosos libros para TASCHEN, entre los que se incluyen 1000 Chairs, Diseño del siglo XX, El diseño industrial de la A a la Z, Scandinavian Design y Diseño del siglo XXI.
De la contraportada
This research monograph is highly contextual in the present era of spatial/spatio-temporal data explosion. The overall text contains many interesting results that are worth applying in practice, while it is also a source of intriguing and motivating questions for advanced research on spatial data science. The monograph is primarily prepared for graduate students of Computer Science, who wish to employ probabilistic graphical models, especially Bayesian networks (BNs), for applied research on spatial/spatio-temporal data. Students of any other discipline of engineering, science, and technology, will also find this monograph useful. Research students looking for a suitable problem for their MS or PhD thesis will also find this monograph beneficial. The open research problems as discussed with sufficient references in Chapter-8 and Chapter-9 can immensely help graduate researchers to identify topics of their own choice. The various illustrations and proofs presented throughout the monograph may help them to better understand the working principles of the models. The present monograph, containing sufficient description of the parameter learning and inference generation process for each enhanced BN model, can also serve as an algorithmic cookbook for the relevant system developers.
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- EditorialSpringer
- Año de publicación2019
- ISBN 10 3030277488
- ISBN 13 9783030277482
- EncuadernaciónTapa dura
- IdiomaInglés
- Número de edición1
- Número de páginas176
- Contacto del fabricanteno disponible
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Enhanced Bayesian Network Models for Spatial Time Series Prediction
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Springer International Publishing, 2019
ISBN 10: 3030277488
ISBN 13: 9783030277482
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Librería: moluna, Greven, Alemania
Calificación del vendedor: 5 de 5 estrellas
Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This is the first text that throws light on the recent advancements in developing enhanced Bayesian network (BN) models to address the various challenges in spatial time series predictionThe monograph covers both theoretical and empirical aspects of a . Nº de ref. del artículo: 335812428
Cantidad disponible: Más de 20 disponibles
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Enhanced Bayesian Network Models for Spatial Time Series Prediction: Recent Research Trend in Data-Driven Predictive Analytics (Studies in Computational Intelligence, 858)
Librería: Ria Christie Collections, Uxbridge, Reino Unido
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Condición: New. In. Nº de ref. del artículo: ria9783030277482_new
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Enhanced Bayesian Network Models for Spatial Time Series Prediction
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Springer International Publishing Nov 2019, 2019
ISBN 10: 3030277488
ISBN 13: 9783030277482
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Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
Calificación del vendedor: 5 de 5 estrellas
Buch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This research monograph is highly contextual in the present era of spatial/spatio-temporal data explosion. The overall text contains many interesting results that are worth applying in practice, while it is also a source of intriguing and motivating questions for advanced research on spatial data science.The monograph is primarily prepared for graduate students of Computer Science, who wish to employ probabilistic graphical models, especially Bayesian networks (BNs), for applied research on spatial/spatio-temporal data. Students of any other discipline of engineering, science, and technology, will also find this monograph useful. Research students looking for a suitable problem for their MS or PhD thesis will also find this monograph beneficial. The open research problems as discussed with sufficient references in Chapter-8 and Chapter-9 can immensely help graduate researchers to identify topics of their own choice. The various illustrations and proofs presented throughout the monograph may help them to better understand the working principles of the models. The present monograph, containing sufficient description of the parameter learning and inference generation process for each enhanced BN model, can also serve as an algorithmic cookbook for the relevant system developers. 176 pp. Englisch. Nº de ref. del artículo: 9783030277482
Cantidad disponible: 2 disponibles
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Enhanced Bayesian Network Models for Spatial Time Series Prediction : Recent Research Trend in Data-Driven Predictive Analytics
Publicado por
Springer International Publishing, 2019
ISBN 10: 3030277488
ISBN 13: 9783030277482
Nuevo
Tapa dura
Librería: AHA-BUCH GmbH, Einbeck, Alemania
Calificación del vendedor: 5 de 5 estrellas
Buch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - This research monograph is highly contextual in the present era of spatial/spatio-temporal data explosion. The overall text contains many interesting results that are worth applying in practice, while it is also a source of intriguing and motivating questions for advanced research on spatial data science.The monograph is primarily prepared for graduate students of Computer Science, who wish to employ probabilistic graphical models, especially Bayesian networks (BNs), for applied research on spatial/spatio-temporal data. Students of any other discipline of engineering, science, and technology, will also find this monograph useful. Research students looking for a suitable problem for their MS or PhD thesis will also find this monograph beneficial. The open research problems as discussed with sufficient references in Chapter-8 and Chapter-9 can immensely help graduate researchers to identify topics of their own choice. The various illustrations and proofs presented throughout the monograph may help them to better understand the working principles of the models. The present monograph, containing sufficient description of the parameter learning and inference generation process for each enhanced BN model, can also serve as an algorithmic cookbook for the relevant system developers. Nº de ref. del artículo: 9783030277482
Cantidad disponible: 1 disponibles
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Enhanced Bayesian Network Models for Spatial Time Series Prediction
Publicado por
Springer International Publishing, Springer International Publishing Nov 2019, 2019
ISBN 10: 3030277488
ISBN 13: 9783030277482
Nuevo
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Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
Calificación del vendedor: 5 de 5 estrellas
Buch. Condición: Neu. Neuware -This research monograph is highly contextual in the present era of spatial/spatio-temporal data explosion. The overall text contains many interesting results that are worth applying in practice, while it is also a source of intriguing and motivating questions for advanced research on spatial data science.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 176 pp. Englisch. Nº de ref. del artículo: 9783030277482
Cantidad disponible: 2 disponibles
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Enhanced Bayesian Network Models for Spatial Time Series Prediction
Librería: Books Puddle, New York, NY, Estados Unidos de America
Calificación del vendedor: 4 de 5 estrellas
Condición: New. pp. XXIII, 149 67 illus., 59 illus. in color. 1 Edition NO-PA16APR2015-KAP. Nº de ref. del artículo: 26384556980
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Enhanced Bayesian Network Models for Spatial Time Series Prediction
Impresión bajo demandaLibrería: Majestic Books, Hounslow, Reino Unido
Calificación del vendedor: 5 de 5 estrellas
Condición: New. Print on Demand pp. XXIII, 149 67 illus., 59 illus. in color. Nº de ref. del artículo: 379347051
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Enhanced Bayesian Network Models for Spatial Time Series Prediction: Recent Research Trend in Data-Driven Predictive Analytics (Studies in Computational Intelligence)
Librería: Revaluation Books, Exeter, Reino Unido
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Hardcover. Condición: Brand New. 172 pages. 9.25x6.10x0.59 inches. In Stock. Nº de ref. del artículo: x-3030277488
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Enhanced Bayesian Network Models for Spatial Time Series Prediction
Impresión bajo demandaLibrería: Biblios, Frankfurt am main, HESSE, Alemania
Calificación del vendedor: 5 de 5 estrellas
Condición: New. PRINT ON DEMAND pp. XXIII, 149 67 illus., 59 illus. in color. Nº de ref. del artículo: 18384556990
Cantidad disponible: 4 disponibles