Enhanced Bayesian Network Models for Spatial Time Series Prediction: Recent Research Trend in Data-Driven Predictive Analytics - Couverture souple

Das, Monidipa; Ghosh, Soumya K.

9783030277512: Enhanced Bayesian Network Models for Spatial Time Series Prediction: Recent Research Trend in Data-Driven Predictive Analytics

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ISBN 10 : 3030277518 ISBN 13 : 9783030277512

Editeur : Springer, 2020

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Introduction.- Standard Bayesian Network Models for Spatial Time Series Prediction.- Bayesian Network with added Residual Correction Mechanism.- Spatial Bayesian Network.- Semantic Bayesian Network.- Advanced Bayesian Network Models with Fuzzy Extension.- Comparative Study of Parameter Learning Complexity.- Spatial Time Series Prediction using Advanced BN Models- An Application Perspective.- Summary and Future Research.

Les informations fournies dans la section � Synopsis � peuvent faire r�f�rence � une autre �dition de ce titre.

�diteurSpringer
Date d'�dition2020
ISBN 10 3030277518
ISBN 13 9783030277512
ReliureBroch�
Langueanglais
Nombre de pages176
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9783030277482: Enhanced Bayesian Network Models for Spatial Time Series Prediction: Recent Research Trend in Data-driven Predictive Analytics

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ISBN 10 : 3030277488 ISBN 13 : 9783030277482
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Enhanced Bayesian Network Models for Spatial Time Series Prediction

Monidipa Das|Soumya K. Ghosh

Edit� par Springer International Publishing, 2020

ISBN 10 : 3030277518 ISBN 13 : 9783030277512

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Etat : 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 . N� de r�f. du vendeur 448677586

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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)

Das, Monidipa; Ghosh, Soumya K.

Edit� par Springer, 2020

ISBN 10 : 3030277518 ISBN 13 : 9783030277512

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Enhanced Bayesian Network Models for Spatial Time Series Prediction : Recent Research Trend in Data-Driven Predictive Analytics

Soumya K. Ghosh

Edit� par Springer International Publishing, 2020

ISBN 10 : 3030277518 ISBN 13 : 9783030277512

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Taschenbuch. Etat : 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. N� de r�f. du vendeur 9783030277512

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Enhanced Bayesian Network Models for Spatial Time Series Prediction

Soumya K. Ghosh

Edit� par Springer International Publishing Nov 2020, 2020

ISBN 10 : 3030277518 ISBN 13 : 9783030277512

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Taschenbuch. Etat : 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. N� de r�f. du vendeur 9783030277512

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Enhanced Bayesian Network Models for Spatial Time Series Prediction

Soumya K. Ghosh

Edit� par Springer International Publishing, Springer International Publishing Nov 2020, 2020

ISBN 10 : 3030277518 ISBN 13 : 9783030277512

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Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne

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Taschenbuch. Etat : 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. N� de r�f. du vendeur 9783030277512

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