Approaches to Bayesian Network Model Construction

  • Ifeyinwa E. Achumba
  • Djamel Azzi
  • Ifeanyi Ezebili
  • Sebastian Bersch
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 229)


Bayesian Network (BN) has sound mathematical basis, enables reasoning under uncertainty, and facilitates the update of beliefs, given new evidence. It also enables the visual representation of a model. These make BN suitable for solving uncertainty problems. This chapter details BN model construction approaches and presents our experiences with selecting the optimal construction approach.


Bayesian networks BN model construction BN model parameterization Parameter learning Performance index Structure learning 



This work was supported in part by the Schlumberger Foundation under its Faculty For The Future (FFTF) scholarship programme aimed at encouraging women, in Science, Engineering and Technology (STE), in their pursuit for academic excellence.


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Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Ifeyinwa E. Achumba
    • 1
  • Djamel Azzi
    • 1
  • Ifeanyi Ezebili
    • 2
  • Sebastian Bersch
    • 1
  1. 1.Faculty of Technology, School of EngineeringUniversity of PortsmouthPortsmouthUK
  2. 2.Department of Electrical and Electronic Engineering, School of Engineering and Engineering TechnologyFederal University of Technology, OwerriOwerriNigeria

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