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Autor(en): 
  • Soumya K. Ghosh
  • Monidipa Das
  • Enhanced Bayesian Network Models for Spatial Time Series Prediction: Recent Research Trend in Data-Driven Predictive Analytics 
     

    (Buch)
    Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 3 Artikel!


    Übersicht

    Auf mobile öffnen
     
    Lieferstatus:   Auf Bestellung (Lieferzeit unbekannt)
    Veröffentlichung:  November 2019  
    Genre:  Naturwissensch., Medizin, Technik 
     
    Applied Dynamical Systems / B / complexity / Computational complexity / Computational Intelligence / Cybernetics & systems theory / Data Engineering / Databases / engineering / Engineering mathematics / Engineering—Data processing / Maths for engineers / Technology# general issues
    ISBN:  9783030277482 
    EAN-Code: 
    9783030277482 
    Verlag:  Springer Nature EN 
    Einband:  Gebunden  
    Sprache:  English  
    Serie:  #858 - Studies in Computational Intelligence  
    Dimensionen:  H 235 mm / B 155 mm / D  
    Gewicht:  436 gr 
    Seiten:  149 
    Illustration:  XXIII, 149 p. 67 illus., 59 illus. in color., schwarz-weiss Illustrationen, farbige Illustrationen 
    Bewertung: Titel bewerten / Meinung schreiben
    Inhalt:
    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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