Approaches to bayesian network model construction

Ifeyinwa E. Achumba; Djamel Azzi; Ifeanyi Ezebili; Sebastian Bersch

Conference Proceedings

Approaches to bayesian network model construction

Achumba I
Azzi D
Ezebili I
et al.

Lecture Notes in Electrical Engineering (2013) 229 LNEE 461-474

DOI: 10.1007/978-94-007-6190-2_35

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Abstract

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. © 2013 Springer Science+Business Media Dordrecht.

Author supplied keywords

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

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Achumba, I. E., Azzi, D., Ezebili, I., & Bersch, S. (2013). Approaches to bayesian network model construction. In Lecture Notes in Electrical Engineering (Vol. 229 LNEE, pp. 461–474). Springer Verlag. https://doi.org/10.1007/978-94-007-6190-2_35

Approaches to bayesian network model construction

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