An inclusion optimal algorithm for chain graph structure learning
Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:778-786, 2014.
This paper presents and proves an extension of Meek’s conjecture to chain graphs under the Lauritzen-Wermuth-Frydenberg interpretation. The proof of the conjecture leads to the development of a structure learning algorithm that finds an inclusion optimal chain graph for any given probability distribution satisfying the composition property. Finally, the new algorithm is experimentally evaluated.