Skip to main content
SpringerLink
Log in

Enhanced Bayesian Network Models for Spatial Time Series Prediction

Recent Research Trend in Data-Driven Predictive Analytics

  • Book
  • © 2020

Overview

Authors:
  1. Monidipa Das

    1. Department of Computer Science and Engineering, Indian Institute of Technology Kharagpur, Kharagpur, India

    View author publications

    You can also search for this author in PubMed   Google Scholar

  2. Soumya K. Ghosh

    1. Department of Computer Science and Engineering, Indian Institute of Technology Kharagpur, Kharagpur, India

    View author publications

    You can also search for this author in PubMed   Google Scholar

  • 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)

This is a preview of subscription content, log in via an institution to check access.

Access this book

Log in via an institution
eBook USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access

Licence this eBook for your library

Institutional subscriptions

Table of contents (9 chapters)

  1. Front Matter

    Pages i-xxiii
    Download chapter PDF

  2. Introduction

    • Monidipa Das, Soumya K. Ghosh
    Pages 1-9

  3. Standard Bayesian Network Models for Spatial Time Series Prediction

    • Monidipa Das, Soumya K. Ghosh
    Pages 11-22

  4. Bayesian Network with Residual Correction Mechanism

    • Monidipa Das, Soumya K. Ghosh
    Pages 23-52

  5. Spatial Bayesian Network

    • Monidipa Das, Soumya K. Ghosh
    Pages 53-79

  6. Semantic Bayesian Network

    • Monidipa Das, Soumya K. Ghosh
    Pages 81-99

  7. Advanced Bayesian Network Models with Fuzzy Extension

    • Monidipa Das, Soumya K. Ghosh
    Pages 101-113

  8. Comparative Study of Parameter Learning Complexities of Enhanced Bayesian Networks

    • Monidipa Das, Soumya K. Ghosh
    Pages 115-124

  9. Spatial Time Series Prediction Using Advanced BN Models—An Application Perspective

    • Monidipa Das, Soumya K. Ghosh
    Pages 125-136

  10. Summary and Future Research

    • Monidipa Das, Soumya K. Ghosh
    Pages 137-142

  11. Back Matter

    Pages 143-149
    Download chapter PDF
Back to top

Keywords

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.

Authors and Affiliations

  • Department of Computer Science and Engineering, Indian Institute of Technology Kharagpur, Kharagpur, India

    Monidipa Das, Soumya K. Ghosh

Bibliographic Information

Publish with us

Policies and ethics

Back to top

Search

Navigation