Example of learning Bayesian networks from simulation data

  • Marcin Bednarski Silesian University of Technology


Bayesian belief networks represent and process probabilistic knowledge. This representation rigorously describes the knowledge of some domains and it is a human easy-use qualitative structure that facilitates communication between a user and a system incorporating the probabilistic model. Learning Bayesian network from data may be grouped into two modelling situations: qualitative learning and quantitative learning. The first one consists in establishing the structure of the network, whereas the second concerns determining parameters of the network (conditional probabilities) . Both modelling methods were applied on exemplary data to show the possibilities and benefits of this methods. The results and conclusions are presented. It was necessary to preprocess the date first. The used method, described in detail in the paper, consists in discretization into linguistic states on the basis of evaluated signal derivative. Some remarks about adjusting the network, as a part of model identification, are also presented.


Bayesian network, learning, diagnostic models,


[l] B. Abramson. The design of Belief Network-based Systems for Price Forecasting. Comput. Elect. Eng., 20, 1994.
[2] S. Andreassen, M. Woldbye, B. Falck, S.K. Andersen. MUNIN: A Casual probabilistic Network for Interpretation of Electromyographic findings. In: Proceedings of the 10-th International Join Conference on Artificial Inteligence, Morgan Kaufmann, San Mateo, Calif., Aug. 1987.
[3] S. Acida, L.M. de Campos, J.M. Fernandez-Luna, S. Rodriguez, J.M. Rodriguez, J .L. Salcedo: A comparison of learning algorithms for Bayesian networks: a case study based on data from an emergency medical service. Artificial Intelligence in Medicine, 30: 215-232, 2004
[4] M. Bednarski, W. Cholewa, W. Frid. Identification of sensitivities in Bayesian networks. Engineering Applications of Artificial Intelligence, 17: 327- 335, 2004
[5] M. Bednarski, W. Cholewa, W. Frid, M. Galek. Simulation of Nuclear Reactors Accidents. Department of Fundamentals of Machine Design, Zeszyt 124, Gliwice, 2004.
Nov 28, 2022
How to Cite
BEDNARSKI, Marcin. Example of learning Bayesian networks from simulation data. Computer Assisted Methods in Engineering and Science, [S.l.], v. 12, n. 2-3, p. 103-110, nov. 2022. ISSN 2956-5839. Available at: <https://cames.ippt.pan.pl/index.php/cames/article/view/980>. Date accessed: 23 june 2024.