Learning Bayesian networks: approaches and issues

TLDR

Bayesian networks are widely used for modeling uncertain knowledge, and learning them from data has become essential because experts struggle to specify them, with recent developments expanding the field. This paper surveys the literature on learning Bayesian network structure from data. The authors review the literature broadly, covering major subfields without delving into detailed methods. The paper compiles a comprehensive reference of relevant publications for sub‑topics in learning Bayesian networks.

Abstract

Abstract Bayesian networks have become a widely used method in the modelling of uncertain knowledge. Owing to the difficulty domain experts have in specifying them, techniques that learn Bayesian networks from data have become indispensable. Recently, however, there have been many important new developments in this field. This work takes a broad look at the literature on learning Bayesian networks—in particular their structure—from data. Specific topics are not focused on in detail, but it is hoped that all the major fields in the area are covered. This article is not intended to be a tutorial—for this, there are many books on the topic, which will be presented. However, an effort has been made to locate all the relevant publications, so that this paper can be used as a ready reference to find the works on particular sub-topics.