Searching for Bayesian Network Structures in the Space of Restricted Acyclic Partially Directed Graphs

TL;DR

This paper introduces a novel local search method for Bayesian network structure learning that operates in a reduced search space of restricted acyclic partially directed graphs (RPDAGs), potentially improving efficiency and solution quality.

Contribution

It proposes a new search space for Bayesian network structure learning based on RPDAGs, which reduces configurations and may find better local optima than traditional DAG-based methods.

Findings

Improved efficiency in structure search.

Potential to find better local optima.

Validated on test problems including Alarm Monitoring System.

Abstract

Although many algorithms have been designed to construct Bayesian network structures using different approaches and principles, they all employ only two methods: those based on independence criteria, and those based on a scoring function and a search procedure (although some methods combine the two). Within the score+search paradigm, the dominant approach uses local search methods in the space of directed acyclic graphs (DAGs), where the usual choices for defining the elementary modifications (local changes) that can be applied are arc addition, arc deletion, and arc reversal. In this paper, we propose a new local search method that uses a different search space, and which takes account of the concept of equivalence between network structures: restricted acyclic partially directed graphs (RPDAGs). In this way, the number of different configurations of the search space is reduced, thus improving efficiency. Moreover, although the final result must necessarily be a local optimum given the nature of the search method, the topology of the new search space, which avoids making early decisions about the directions of the arcs, may help to find better local optima than those obtained by searching in the DAG space. Detailed results of the evaluation of the proposed search method on several test problems, including the well-known Alarm Monitoring System, are also presented.