1. Cooper, G.F., Herskovits, E.: A bayesian method for the induction of probabilistic networks from data. Mach. Learn. 9(4), 309–347 (1992)
2. de Campos, C.P., Zeng, Z., Ji, Q.: Structure learning of bayesian networks using constraints. In: Proceedings of the 26th Annual International Conference on Machine Learning, ICML 2009, pp. 113–120. ACM, New York, NY, USA (2009)
3. Friedman, N., Getoor, L., Koller, D., Pfeffer, A.: Learning probabilistic relational models. In: Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence, IJCAI 1999, Stockholm, Sweden, July 31 - August 6, 1999. 2 Volumes, 1450 pages, pp. 1300–1309 (1999)
4. Liang, C., Forbus, K.D.: Learning plausible inferences from semantic web knowledge by combining analogical generalization with structured logistic regression. In: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, AAAI 2015, pp. 551–557. AAAI Press (2015)
5. Madigan, D., Andersson, S.A., Perlman, M.D., Volinsky, C.T.: Bayesian model averaging and model selection for markov equivalence classes of acyclic digraphs. Commun. Stat.-Theory Methods 25(11), 2493–2519 (1996)