Overview
- 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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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.
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Keywords
Table of contents (9 chapters)
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Front Matter
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Back Matter
Authors and Affiliations
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