  In recent years there has been significant progress in algorithms and methods
for inducing Bayesian networks from data. However, in complex data analysis
problems, we need to go beyond being satisfied with inducing networks with high
scores. We need to provide confidence measures on features of these networks:
Is the existence of an edge between two nodes warranted? Is the Markov blanket
of a given node robust? Can we say something about the ordering of the
variables? We should be able to address these questions, even when the amount
of data is not enough to induce a high scoring network. In this paper we
propose Efron's Bootstrap as a computationally efficient approach for answering
these questions. In addition, we propose to use these confidence measures to
induce better structures from the data, and to detect the presence of latent
variables.
