Data Analysis with Bayesian Networks: A Bootstrap Approach
This work addresses the need for robust uncertainty quantification in Bayesian network analysis for data scientists, though it appears incremental as it applies an existing statistical method to a known bottleneck.
The paper tackles the problem of providing confidence measures for features in Bayesian networks, such as edge existence and Markov blanket robustness, even with limited data, by proposing Efron's Bootstrap as a computationally efficient approach, and it shows that this method can improve structure induction and detect latent variables.
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.