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.
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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