Data Analysis with Bayesian Networks: A Bootstrap Approach

TL;DR

This paper introduces a bootstrap-based method to assess confidence in features of Bayesian networks, enhancing structure learning and latent variable detection in complex data analysis.

Contribution

It proposes using Efron's Bootstrap to provide confidence measures for Bayesian network features, improving robustness and interpretability of learned structures.

Findings

Bootstrap provides reliable confidence estimates for network features

Confidence measures help detect latent variables

Method improves structure induction in limited data scenarios

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.