R. Bouckaert, Bayesian belief networks: from inference to construction, 1995.

W. Buntine, Theory Refinement on Bayesian Networks, Uncertainty Proceedings 1991, pp.52-60, 1991.
DOI : 10.1016/B978-1-55860-203-8.50010-3

URL : http://www-cad.eecs.berkeley.edu/~wray/Mirror/bayes.ps.Z

X. Chen, G. Anantha, L. , and X. , Improving Bayesian Network Structure Learning with Mutual Information-Based Node Ordering in the K2 Algorithm, IEEE Transactions on Knowledge and Data Engineering, vol.20, issue.5, pp.628-640, 2008.
DOI : 10.1109/TKDE.2007.190732

J. Cheng, D. A. Bell, and W. Liu, Learning belief networks from data, Proceedings of the sixth international conference on Information and knowledge management , CIKM '97, pp.325-331, 1997.
DOI : 10.1145/266714.266920

D. M. Chickering, Learning Bayesian networks is NP-complete. Learning from data: Artificial intelligence and statistics V, pp.121-130, 1996.
DOI : 10.1007/978-1-4612-2404-4_12

URL : http://research.microsoft.com/%7Edmax/publications/lns96.pdf

C. Chow and C. Liu, Approximating discrete probability distributions with dependence trees, IEEE Transactions on Information Theory, vol.14, issue.3, pp.462-467, 1968.
DOI : 10.1109/TIT.1968.1054142

URL : http://www.cs.iastate.edu/~honavar/chou-liu.pdf

G. F. Cooper and E. Herskovits, A Bayesian method for the induction of probabilistic networks from data, Machine Learning, pp.309-347, 1992.
DOI : 10.1007/978-1-4613-2283-2

J. Davis and P. Domingos, Bottom-up learning of Markov network structure, Proceedings of the 27th International Conference on Machine Learning (ICML-10), pp.271-278, 2010.

C. P. De-campos and Q. Ji, Efficient structure learning of Bayesian networks using constraints, Journal of Machine Learning Research, vol.12, pp.663-689, 2011.

S. R. De-morais, A. Aussem, and M. Corbex, Handling almost-deterministic relationships in constraint-based Bayesian network discovery: Application to cancer risk factor identification, European Symposium on Artificial Neural Networks, ESANN'08, 2008.
URL : https://hal.archives-ouvertes.fr/hal-00266064

D. Dheeru, K. Taniskidou, and E. , UCI machine learning repository, 2017.

E. Kaed, C. Leida, B. Gray, and T. , Building management insights driven by a multi-system semantic representation approach, 2016 IEEE 3rd World Forum on Internet of Things (WF-IoT), pp.520-525, 2016.
DOI : 10.1109/WF-IoT.2016.7845433

N. Friedman, I. Nachman, and D. Peér, Learning Bayesian network structure from massive datasets: the 'sparse candidate 'algorithm, Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence, pp.206-215, 1999.

D. Heckerman, D. Geiger, C. , and D. M. , Learning Bayesian networks: The combination of knowledge and statistical data, Machine Learning, pp.197-243, 1995.

Y. Huhtala, J. Kärkkäinen, P. Porkka, and H. Toivonen, Tane: An efficient algorithm for discovering functional and approximate dependencies. The computer journal, pp.100-111, 1999.
DOI : 10.1093/comjnl/42.2.100

URL : http://www3.oup.co.uk/computer_journal/hdb/Volume_42/Issue_02/pdf/420100.pdf

D. Koller and N. Friedman, Probabilistic graphical models: principles and techniques, 2009.

D. D. Koo, J. J. Lee, A. Sebastiani, K. , and J. , An Internet-of-Things (IoT) System Development and Implementation for Bathroom Safety Enhancement, Procedia Engineering, vol.145, pp.396-403, 2016.
DOI : 10.1016/j.proeng.2016.04.004

W. Luo, Learning Bayesian networks in semideterministic systems, Canadian Conference on AI, pp.230-241, 2006.

A. Mabrouk, C. Gonzales, K. Jabet-chevalier, C. , and E. , An efficient Bayesian network structure learning algorithm in the presence of deterministic relations, Proceedings of the Twenty-first European Conference on Artificial Intelligence, pp.567-572, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01215671

S. Nie, C. P. De-campos, J. , and Q. , Learning Bayesian networks with bounded tree-width via guided search, AAAI, pp.3294-3300, 2016.
DOI : 10.1016/j.ijar.2016.07.002

URL : https://pure.qub.ac.uk/portal/files/62509783/bnsl.pdf

T. Papenbrock, J. Ehrlich, J. Marten, T. Neubert, J. Rudolph et al., Functional dependency discovery, Proceedings of the VLDB Endowment, pp.1082-1093, 2015.
DOI : 10.14778/2794367.2794377

A. Rényi, On measures of entropy and information, Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics and Probability, 1961.

M. Scanagatta, G. Corani, C. P. De-campos, and M. Zaffalon, Learning treewidth-bounded Bayesian networks with thousands of variables, Advances in Neural Information Processing Systems, pp.1462-1470, 2016.

M. Scanagatta, C. P. De-campos, G. Corani, and M. Zaffalon, Learning Bayesian networks with thousands of variables, Advances in Neural Information Processing Systems, pp.1864-1872, 2015.

G. Schwarz, Estimating the Dimension of a Model, The Annals of Statistics, vol.6, issue.2, pp.461-464, 1978.
DOI : 10.1214/aos/1176344136

M. Scutari, Learning Bayesian networks with the bnlearn R package, 2009.

T. Silander and P. Myllymaki, A simple approach for finding the globally optimal Bayesian network structure. arXiv preprint, 2012.

P. Spirtes, C. N. Glymour, and R. Scheines, Causation, prediction, and search, 2000.
DOI : 10.1007/978-1-4612-2748-9

M. Teyssier and D. Koller, Ordering-based search: A simple and effective algorithm for learning Bayesian networks, Proceedings of the 28th conference on Uncertainty in artificial intelligence, 2012.

S. Yaramakala and D. Margaritis, Speculative Markov Blanket Discovery for Optimal Feature Selection, Fifth IEEE International Conference on Data Mining (ICDM'05), p.4, 2005.
DOI : 10.1109/ICDM.2005.134

URL : http://www.cs.iastate.edu/~dmarg/Papers/Yaramakala-Margaritis-ICDM05.pdf

C. Yuan and B. Malone, Learning optimal Bayesian networks: A shortest path perspective, Journal of Artificial Intelligence Research, 2013.