On the Use of Skeletons when Learning in Bayesian Networks

TL;DR

This paper introduces a heuristic operator that optimizes edge orientations in Bayesian networks by alternating between DAGs and skeletons, improving structure learning with a scoring-based approach.

Contribution

It presents a novel operator for Bayesian network structure learning that enhances edge orientation optimization by combining DAG and skeleton spaces.

Findings

Effective in artificial data experiments

Improves structure learning accuracy

Applicable to real-world datasets

Abstract

In this paper, we present a heuristic operator which aims at simultaneously optimizing the orientations of all the edges in an intermediate Bayesian network structure during the search process. This is done by alternating between the space of directed acyclic graphs (DAGs) and the space of skeletons. The found orientations of the edges are based on a scoring function rather than on induced conditional independences. This operator can be used as an extension to commonly employed search strategies. It is evaluated in experiments with artificial and real-world data.