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by Jiming Liu, Michel C. Desmarais
IEEE Transactions on Knowledge and Data Engineering
http://www.crim.ca/.ipsi/articles/method96.ps
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Abstract:
This paper describes an algorithmic means for inducing implication networks from empirical data samples. The induced network enables efficient inferences about the values of network nodes if certain observations are made. This implication induction method is approximate in nature as probablistic network requirements are relaxed in the construction of dependence relationships based on statistical testing. In order to examine the effectiveness and validity of the induction method, several Monte-Carlo simulations were conducted where theoretical Bayesian networks were used to generate empirical data samples \Gamma some of which were used to induce implication relations whereas others were used to verify the results of evidential reasoning with the induced networks. The values in the implication networks were predicted by applying a modified version of Dempster-Shafer belief updating scheme. The results of predictions were, furthermore, compared to the ones generated by Pearl's stochastic simulation method [21], a probabilistic reasoning method that operates directly on the theoretical Bayesian networks. The comparisons consistently show that the results of predictions based on the induced networks would be comparable to those generated by Pearl's method when reasoning in a variety of uncertain knowledge domains \Gamma ones that were simulated using the presumed theoretical probabilistic networks of different 1
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