Structure learning of Bayesian networks using constraints

This paper addresses exact learning of Bayesian network structure from data and expert's knowledge based on score functions that are decomposable. First, it describes useful properties that strongly reduce the time and memory costs of many known methods, such as hill-climbing, dynamic programming and sampling methods. Secondly, a branch and bound algorithm is presented that integrates parameter and structural constraints with data in a way to guarantee global optimality with respect to the score function. It is an any-time procedure because, if stopped, it provides the best current solution and an estimation about how far it is from the global solution. We show empirically the advantages of the properties and the constraints, and the applicability of the algorithm to large data sets (up to one hundred variables) that cannot be handled by other current techniques (limited to around 30 variables).

• Type: Conference
• Year: 2009
• Where: ICML
• Authors: Cassio Polpo de Campos, Zhi Zeng, Qiang Ji
• Keywords: Bayesian Network Structure, Global Optimality, ICML 2009, Machine Learning, Structural Constraints