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Enhanced Bayesian network models for...

Das, Monidipa.

Enhanced Bayesian network models for spatial time series predictionrecent research trend in data-driven predictive analytics /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Enhanced Bayesian network models for spatial time series predictionby Monidipa Das, Soumya K. Ghosh.
Reminder of title:	recent research trend in data-driven predictive analytics /
Author:	Das, Monidipa.
other author:	Ghosh, Soumya K.
Published:	Cham :Springer International Publishing :2020.
Description:	xxiii, 149 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Time-series analysis.
Online resource:	https://doi.org/10.1007/978-3-030-27749-9
ISBN:	9783030277499$q(electronic bk.)

Enhanced Bayesian network models for spatial time series predictionrecent research trend in data-driven predictive analytics /
Das, Monidipa.

Enhanced Bayesian network models for spatial time series predictionrecent research trend in data-driven predictive analytics /[electronic resource] :by Monidipa Das, Soumya K. Ghosh. - Cham :Springer International Publishing :2020. - xxiii, 149 p. :ill., digital ;24 cm. - Studies in computational intelligence,v.8581860-949X ;. - Studies in computational intelligence ;v. 216..

Introduction -- Standard Bayesian Network Models for Spatial Time Series Prediction -- Bayesian Network with added Residual Correction Mechanism -- Spatial Bayesian Network -- Semantic Bayesian Network -- Advanced Bayesian Network Models with Fuzzy Extension -- Comparative Study of Parameter Learning Complexity -- Spatial Time Series Prediction using Advanced BN Models- An Application Perspective -- Summary and Future Research.

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.

ISBN: 9783030277499$q(electronic bk.)

Standard No.: 10.1007/978-3-030-27749-9doiSubjects--Topical Terms:

181890
Time-series analysis.

LC Class. No.: QA280 / .D376 2020

Dewey Class. No.: 519.55

Enhanced Bayesian network models for spatial time series predictionrecent research trend in data-driven predictive analytics /
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505 0 $a Introduction -- Standard Bayesian Network Models for Spatial Time Series Prediction -- Bayesian Network with added Residual Correction Mechanism -- Spatial Bayesian Network -- Semantic Bayesian Network -- Advanced Bayesian Network Models with Fuzzy Extension -- Comparative Study of Parameter Learning Complexity -- Spatial Time Series Prediction using Advanced BN Models- An Application Perspective -- Summary and Future Research.
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