1. Ankan, A., Panda, A.: pgmpy: Probabilistic graphical models using Python. In: Proceedings of the 14th Python in Science Conference (SCIPY 2015). Citeseer (2015).
https://doi.org/10.25080/Majora-7b98e3ed-001
2. Communications in Computer and Information Science;MEGV Cattaneo,2014
3. Correia, A.H., Cussens, J., de Campos, C.P.: On pruning for score-based Bayesian network structure learning. arXiv preprint
arXiv:1905.09943
(2019)
4. Correia, A.H.C., de Campos, C.P., van der Gaag, L.C.: An experimental study of prior dependence in Bayesian network structure learning. In: International Symposium on Imprecise Probabilities: Theories and Applications, pp. 78–81 (2019)
5. Couso, I., Moral, S.: Sets of desirable gambles: conditioning, representation, and precise probabilities. Int. J. Approximate Reasoning 52(7), 1034–1055 (2011).
https://doi.org/10.1016/j.ijar.2011.04.004