ISBN: 9783030277512 - enhanced bayesian network models for spatial time series prediction: recent research trend in data-driven predictive analytics: 858 (15 risultati)

paperback. Condizione: Sehr gut. 176 Seiten; 9783030277512.2 Gewicht in Gramm: 1.

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Taschenbuch. 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.…

Condizione: New. 1st ed. 2020 edition NO-PA16APR2015-KAP.

Paperback. Condizione: Brand New. 176 pages. 9.25x6.10x0.42 inches. In Stock.

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Condizione: new. Questo è un articolo print on demand.

Taschenbuch. 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.…

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 .…

Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. 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 KG, Sachsenplatz 4-6, 1201 Wien 176 pp. Englisch.…

Condizione: New. Print on Demand.

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