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

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

Proc. Seventh International Workshop on Artificial Intelligence and Statistics, 1999.



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.