Data Analysis with Bayesian Networks: A Bootstrap Approach

In recent years there ha� been significant progress in algorithms and methods for inducing Bayesian networks from data. However, in com­ plex data analysis problems, we need to go be­ yond being satisfied with inducing networks with high scores. We need to provide confidence mea­ sures on features of these networks: Is the exis­ tence 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 a� a compu­ tationally 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.