Data Analysis with Bayesian Networks: A Bootstrap Approach
N. Friedman, M. Goldszmidt, and A. Wyner.
To appear in Proc. Fifteenth Conf. on Uncertainty in Artificial Intelligence
Abstract 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.
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