Authors:
- 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
Part of the book series: Studies in Computational Intelligence (SCI, volume 858)
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Table of contents (9 chapters)
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Front Matter
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Back Matter
About this book
Authors and Affiliations
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Department of Computer Science and Engineering, Indian Institute of Technology Kharagpur, Kharagpur, India
Monidipa Das, Soumya K. Ghosh
Bibliographic Information
Book Title: Enhanced Bayesian Network Models for Spatial Time Series Prediction
Book Subtitle: Recent Research Trend in Data-Driven Predictive Analytics
Authors: Monidipa Das, Soumya K. Ghosh
Series Title: Studies in Computational Intelligence
DOI: https://doi.org/10.1007/978-3-030-27749-9
Publisher: Springer Cham
eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)
Copyright Information: Springer Nature Switzerland AG 2020
Hardcover ISBN: 978-3-030-27748-2Published: 19 November 2019
Softcover ISBN: 978-3-030-27751-2Published: 19 November 2020
eBook ISBN: 978-3-030-27749-9Published: 07 November 2019
Series ISSN: 1860-949X
Series E-ISSN: 1860-9503
Edition Number: 1
Number of Pages: XXIII, 149
Number of Illustrations: 8 b/w illustrations, 59 illustrations in colour
Topics: Computational Intelligence, Data Engineering, Complexity, Engineering Mathematics