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Enhanced Bayesian Network Models for Spatial Time Series Prediction - Recent Research Trend in Data-Driven Predictive Analytics (Hardcover, 1st ed. 2020)
Loot Price: R4,130
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Enhanced Bayesian Network Models for Spatial Time Series Prediction - Recent Research Trend in Data-Driven Predictive Analytics (Hardcover, 1st ed. 2020)
Series: Studies in Computational Intelligence, 858
Expected to ship within 12 - 17 working days
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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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