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Enhanced Bayesian Network Models for Spatial Time Series Prediction: Recent Research Trend in Data-driven Predictive Analytics: 858 - Rilegato
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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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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.
Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.
- EditoreSpringer-Nature New York Inc
- Data di pubblicazione2019
- ISBN 10 3030277488
- ISBN 13 9783030277482
- RilegaturaCopertina rigida
- LinguaInglese
- Numero edizione1
- Numero di pagine176
- Contatto del produttorenon disponibile
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Enhanced Bayesian Network Models for Spatial Time Series Prediction
Editore:
Springer International Publishing, 2019
ISBN 10: 3030277488
ISBN 13: 9783030277482
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Da: moluna, Greven, Germania
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Condizione: 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 . Codice articolo 335812428
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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)
Da: Ria Christie Collections, Uxbridge, Regno Unito
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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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Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
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Buch. Condizione: 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. Codice articolo 9783030277482
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Enhanced Bayesian Network Models for Spatial Time Series Prediction : Recent Research Trend in Data-Driven Predictive Analytics
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Springer International Publishing, 2019
ISBN 10: 3030277488
ISBN 13: 9783030277482
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Buch. Condizione: 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. Codice articolo 9783030277482
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Enhanced Bayesian Network Models for Spatial Time Series Prediction
ISBN 10: 3030277488
ISBN 13: 9783030277482
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Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
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Buch. Condizione: 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. Codice articolo 9783030277482
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Enhanced Bayesian Network Models for Spatial Time Series Prediction
Da: Books Puddle, New York, NY, U.S.A.
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Condizione: New. pp. XXIII, 149 67 illus., 59 illus. in color. 1 Edition NO-PA16APR2015-KAP. Codice articolo 26384556980
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Enhanced Bayesian Network Models for Spatial Time Series Prediction
Print on DemandDa: Majestic Books, Hounslow, Regno Unito
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Condizione: New. Print on Demand pp. XXIII, 149 67 illus., 59 illus. in color. Codice articolo 379347051
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Enhanced Bayesian Network Models for Spatial Time Series Prediction
Print on DemandDa: Biblios, Frankfurt am main, HESSE, Germania
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Condizione: New. PRINT ON DEMAND pp. XXIII, 149 67 illus., 59 illus. in color. Codice articolo 18384556990
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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)
Da: Revaluation Books, Exeter, Regno Unito
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Hardcover. Condizione: Brand New. 172 pages. 9.25x6.10x0.59 inches. In Stock. Codice articolo x-3030277488
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