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Studies in Computational Intelligence
cover

Enhanced Bayesian Network Models for Spatial Time Series Prediction

Recent Research Trend in Data-Driven Predictive Analytics

Authors: Das, Monidipa, Ghosh, Soumya K.

  • 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 prediction
    The monograph covers both theoretical and empirical aspects of a number of enhanced Bayesian network models, in a lucid, precise, and highly comprehensive manner
    The monograph includes plenty of illustrative examples and proofs which will immensely help the reader to better understand the working principles of the enhanced BN models. 
    The open research problems as discussed (in Chapter-8 and Chapter-9) along with sufficient allusions can enormously help the graduate researchers to identify topics of their own choice
    The detailed case studies on climatological and hydrological time series prediction, covered throughout the monograph, are expected to grow interest in the BN-based prediction models and to further explore their potentiality to solve problems from similar domains

Buy this book

eBook $109.00
price for USA in USD (gross)
  • ISBN 978-3-030-27749-9
  • Digitally watermarked, DRM-free
  • Included format:
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $149.99
price for USA in USD
  • ISBN 978-3-030-27748-2
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
About this book

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.

Table of contents (9 chapters)

Table of contents (9 chapters)
  • Introduction

    Pages 1-9

    Das, Monidipa (et al.)

  • Standard Bayesian Network Models for Spatial Time Series Prediction

    Pages 11-22

    Das, Monidipa (et al.)

  • Bayesian Network with Residual Correction Mechanism

    Pages 23-52

    Das, Monidipa (et al.)

  • Spatial Bayesian Network

    Pages 53-79

    Das, Monidipa (et al.)

  • Semantic Bayesian Network

    Pages 81-99

    Das, Monidipa (et al.)

Buy this book

eBook $109.00
price for USA in USD (gross)
  • ISBN 978-3-030-27749-9
  • Digitally watermarked, DRM-free
  • Included format:
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $149.99
price for USA in USD
  • ISBN 978-3-030-27748-2
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
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Bibliographic Information

Bibliographic Information
Book Title
Enhanced Bayesian Network Models for Spatial Time Series Prediction
Book Subtitle
Recent Research Trend in Data-Driven Predictive Analytics
Authors
Series Title
Studies in Computational Intelligence
Series Volume
858
Copyright
2020
Publisher
Springer International Publishing
Copyright Holder
Springer Nature Switzerland AG
eBook ISBN
978-3-030-27749-9
DOI
10.1007/978-3-030-27749-9
Hardcover ISBN
978-3-030-27748-2
Series ISSN
1860-949X
Edition Number
1
Number of Pages
XXIII, 149
Number of Illustrations
8 b/w illustrations, 59 illustrations in colour
Topics