An Algorithm for the Construction of Bayesian Network Structures from Data

TL;DR

This paper introduces a novel algorithm for constructing Bayesian network structures from data that combines conditional independence tests with an ordering-based approach, improving upon previous methods.

Contribution

The proposed algorithm uniquely integrates CI tests with an ordering-based method to efficiently recover Bayesian network structures from data.

Findings

Preliminary evaluation on ALARM and LED networks shows promising results.

The algorithm effectively combines CI tests and node ordering for structure learning.

Discussion of performance issues and open problems guides future research.

Abstract

Previous algorithms for the construction of Bayesian belief network structures from data have been either highly dependent on conditional independence (CI) tests, or have required an ordering on the nodes to be supplied by the user. We present an algorithm that integrates these two approaches - CI tests are used to generate an ordering on the nodes from the database which is then used to recover the underlying Bayesian network structure using a non CI based method. Results of preliminary evaluation of the algorithm on two networks (ALARM and LED) are presented. We also discuss some algorithm performance issues and open problems.