On the Application of The Bootstrap for Computing Confidence Measures on Features of Induced Bayesian Networks

N. Friedman, M. Goldszmidt, and A. Wyner.

To appear in Proc.~Seventh International Workshop on Artificial Intelligence and Statistics, 1999.

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In the context of learning Bayesian networks from data, very little work has been published on methods for assessing the quality of an induced model. This issue, however, has received a great deal of attention in the statistics literature. In this paper, we take a well-known method from statistics, Efron's Bootstrap, and examine its applicability for assessing a confidence measure on features of the learned network structure. We also compare this method to assessments based on a practical realization of the Bayesian methodology.

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