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.

本文旨在提出解决复杂数据分析问题中的信心度测量的方法，包括判断两个节点之间的边缘是否成立、一个给定节点的马尔可夫毯子是否鲁棒以及变量排序等方面，并通过 Efron 的 Bootstrap 方法来实现对这些问题的计算高效求解。此外，作者提出使用这些信心度测量来从数据中产生更好的结构，以及检测潜在变量的存在。