Example of learning Bayesian networks from simulation data

Abstract

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.