G. Susanne, C. Bøttcher, and . Dethlefsen, deal: A package for learning Bayesian networks, 2003.

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

W. Buntine, Theory refinement on Bayesian networks, Uncertainty Proceedings, pp.52-60, 1991.

G. Celeux and J. Durand, Selecting hidden markov model state number with cross-validated likelihood, Computational Statistics, vol.23, issue.4, pp.541-564, 2008.
URL : https://hal.archives-ouvertes.fr/inria-00193098

X. Chen, G. Anantha, and X. Lin, 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.

J. Cheng, A. David, W. Bell, and . Liu, Learning belief networks from data: An information theory based approach, Proceedings of the sixth international conference on Information and knowledge management, pp.325-331, 1997.

D. Maxwell and C. , A transformational characterization of equivalent Bayesian network structures, Proceedings of Eleventh Conference on Uncertainty in Artificial Intelligence, R Montreal, pp.87-98, 1995.

D. Maxwell and C. , Learning from data: Artificial intelligence and statistics V, vol.112, pp.121-130, 1996.

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.

R. Barry, R. Cobb, A. Rumí, and . Salmerón, Bayesian network models with discrete and continuous variables, Advances in probabilistic graphical models, pp.81-102, 2007.

G. F. Cooper, The computational complexity of probabilistic inference using Bayesian belief networks, Artif. Intell, vol.42, issue.2-3, pp.393-405, 1990.

F. Gregory, E. Cooper, and . Herskovits, A Bayesian method for the induction of probabilistic networks from data, Machine Learning, vol.9, pp.309-347, 1992.

R. G. Cowell, Conditions under which conditional independence and scoring methods lead to identical selection of Bayesian network models, Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence, UAI'01, pp.91-97, 2001.

J. Cussens, Bayesian network learning with cutting planes, Proceedings of the TwentySeventh Conference on Uncertainty in Artificial Intelligence, UAI'11, pp.153-160, 2011.

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.

M. Cassio-p-de-campos, G. Scanagatta, M. Corani, and . Zaffalon, Entropy-based pruning for learning Bayesian networks using bic, Artificial Intelligence, vol.260, pp.42-50, 2018.

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

. Luis-m-de-campos, A scoring function for learning Bayesian networks based on mutual information and conditional independence tests, Journal of Machine Learning Research, vol.7, pp.2149-2187, 2006.

S. Rodrigues-de-morais, A. Aussem, and M. Corbex, Handling almostdeterministic 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

T. Dean and K. Kanazawa, A model for reasoning about persistence and causation

, Comput. Intell, vol.5, issue.3, pp.142-150, 1989.

R. Dechter, Bucket elimination: A unifying framework for reasoning, Artificial Intelligence, vol.113, issue.1-2, pp.41-85, 1999.

C. Desdouits, J. Bergerand, P. Berseneff, C. L. Pape, and D. Yanculovici, Energy study of a manufacturing plant. ECEEE Industrial Efficiency Summer Study, 2016.

D. Dheeru and . Efi-karra-taniskidou, UCI machine learning repository, 2017.

M. Diaz, G. Juan, O. Lucas, and A. Ryuga, Big data on the internet of things: An example for the e-health, Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS), pp.898-900, 2012.

C. E. Kaed, B. Leida, and T. Gray, Building management insights driven by a multi-system semantic representation approach, Internet of Things (WF-IoT), pp.520-525, 2016.

N. Etherden, A. Kim-johansson, U. Ysberg, K. Kvamme, D. Pampliega et al., Enhanced lv supervision by combining data from meters, secondary substation measurements and medium voltage supervisory control and data acquisition, CIRED-Open Access Proceedings Journal, vol.2017, issue.1, pp.1089-1093, 2017.

J. Friedman, T. Hastie, and R. Tibshirani, The elements of statistical learning, Springer series in statistics, vol.1, 2001.

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.

T. Fu, A review on time series data mining, Engineering Applications of Artificial Intelligence, vol.24, issue.1, pp.164-181, 2011.

R. Fung and K. Chang, Weighing and integrating evidence for stochastic simulation in Bayesian networks, Machine Intelligence and Pattern Recognition, vol.10, pp.209-219

D. Heckerman, A tutorial on learning with Bayesian networks, Learning in graphical models, pp.301-354, 1998.

D. Heckerman, D. Geiger, and D. M. Chickering, Learning Bayesian networks: The combination of knowledge and statistical data, Machine Learning, vol.20, issue.3, pp.197-243, 1995.

, Propagating uncertainty in Bayesian networks by probabilistic logic sampling, Machine Intelligence and Pattern Recognition, vol.5, pp.149-163, 1988.

Y. Huhtala, J. Kärkkäinen, P. Porkka, and H. Toivonen, Tane: An efficient algorithm for discovering functional and approximate dependencies, The computer journal, vol.42, issue.2, pp.100-111, 1999.

Z. Michael-i-jordan, T. S. Ghahramani, L. Jaakkola, and . Saul, An introduction to variational methods for graphical models, Machine learning, vol.37, issue.2, pp.183-233, 1999.

J. Kim and J. Pearl, A computational model for causal and diagnostic reasoning in inference systems, International Joint Conference on Artificial Intelligence, pp.0-0, 1983.

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

D. Dan, J. J. Koo, A. Lee, J. Sebastiani, and . Kim, An internet-of-things (iot) system development and implementation for bathroom safety enhancement, Procedia Engineering, vol.145, pp.396-403, 2016.

S. Kullback, A. Richard, and . Leibler, On information and sufficiency. The annals of mathematical statistics, vol.22, pp.79-86, 1951.

V. L. Tosa, S. Marié, F. Bernier, and D. Piette, Pervasive energy measurements for buildings monitoring, Proceedings of the second Workshop on eeBuildings Data Models, 2011.

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

A. Mabrouk, C. Gonzales, K. Jabet-chevalier, and E. Chojnacki, 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

N. Mamoulis, L. Liu, M. Tamer, and . Özsu, Temporal Data Mining, pp.2948-2952, 2009.

N. Metropolis, A. W. Rosenbluth, N. Marshall, A. H. Rosenbluth, E. Teller et al., Equation of state calculations by fast computing machines, The journal of chemical physics, vol.21, issue.6, pp.1087-1092, 1953.

K. P. , M. , and S. Russell, Dynamic bayesian networks: representation, inference and learning, 2002.

K. P. Murphy, Machine Learning: A Probabilistic Perspective, 2012.

R. Nagarajan, M. Scutari, and S. Lèbre, Bayesian networks in r, vol.122, pp.125-127, 2013.

H. Najmeddine, F. Suard, A. Jay, P. Marechal, and M. Sylvain, Mesures de similarité pour l'aide à l'analyse des données énergétiques de bâtiments, RFIA 2012 (Reconnaissance des Formes et Intelligence Artificielle), pp.978-980, 2012.

P. Le-t-nguyen, W. Wu, W. Chan, Y. Peng, and . Zhang, Predicting collective sentiment dynamics from time-series social media, Proceedings of the first international workshop on issues of sentiment discovery and opinion mining, 2012.

S. Nie, C. Polpo-de-campos, and Q. Ji, Learning Bayesian networks with bounded tree-width via guided search, AAAI, pp.3294-3300, 2016.

D. Pál, B. Póczos, and C. Szepesvári, Estimation of rényi entropy and mutual information based on generalized nearest-neighbor graphs, Advances in Neural Information Processing Systems, pp.1849-1857, 2010.

T. Papenbrock, J. Ehrlich, J. Marten, T. Neubert, J. Rudolph et al., Functional dependency discovery: An experimental evaluation of seven algorithms, Proceedings of the VLDB Endowment, vol.8, pp.1082-1093, 2015.

J. Pearl, Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference

J. Pearl, Causal inference in statistics: An overview, Statistics surveys, vol.3, pp.96-146, 2009.

P. Pflaum, M. Alamir, and M. Y. Lamoudi, Probabilistic energy management strategy for ev charging stations using randomized algorithms, IEEE Transactions on Control Systems Technology, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01765663

K. Ralph, The data warehouse toolkit: Practical techniques for building dimensional data warehouses, 1996.

C. Edward-rasmussen and Z. Ghahramani, Occam's razor, Advances in neural information processing systems, pp.294-300, 2001.

A. Rényi, On measures of entropy and information, Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics and Probability, vol.1, 1961.

D. Rusakov and D. Geiger, Asymptotic model selection for naive Bayesian networks, Journal of Machine Learning Research, vol.6, pp.1-35, 2005.

K. Lawrence, T. Saul, and M. Jaakkola, Mean field theory for sigmoid belief networks, Journal of artificial intelligence research, vol.4, pp.61-76, 1996.

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

M. Scanagatta, G. Corani, M. Cassio-p-de-campos, and . Zaffalon, Learning treewidthbounded Bayesian networks with thousands of variables, Advances in Neural Information Processing Systems, pp.1462-1470, 2016.

R. Scheines, P. Spirtes, C. Glymour, C. Meek, and T. Richardson, Tetrad 3: Tools for causal modeling-user's manual. CMU Philosophy, 1996.

G. Schwarz, Estimating the dimension of a model, The Annals of Statistics, vol.6, issue.2, pp.461-464, 1978.

M. Scutari, Learning Bayesian networks with the bnlearn R package, Journal of Statistical Software, vol.35, issue.3, pp.1-22, 2010.

D. Ross, . Shachter, A. Mark, and . Peot, Simulation approaches to general probabilistic inference on belief networks, Machine Intelligence and Pattern Recognition, vol.10, pp.221-231

T. Silander and P. Myllymäki, A simple approach for finding the globally optimal Bayesian network structure, Proceedings of the Twenty-Second Conference on Uncertainty in Artificial Intelligence, UAI'06, pp.0-9749039, 2006.

T. Silander, P. Kontkanen, and P. Myllymäki, On sensitivity of the map Bayesian network structure to the equivalent sample size parameter, Proceedings of the Twenty-Third Conference on Uncertainty in Artificial Intelligence, pp.360-367, 2007.

J. David, . Spiegelhalter, and . Steffen-l-lauritzen, Sequential updating of conditional probabilities on directed graphical structures, Networks, vol.20, issue.5, pp.579-605, 1990.

P. Spirtes, N. Clark, R. Glymour, and . Scheines, Causation, prediction, and search, 2000.

M. Teyssier and D. Koller, Ordering-based search: a simple and effective algorithm for learning Bayesian networks, Proceedings of the Twenty-First Conference on Uncertainty in Artificial Intelligence, pp.584-590, 2005.

R. Tibshirani, Regression shrinkage and selection via the lasso, Journal of the Royal Statistical Society (Series B), vol.58, pp.267-288, 1996.

I. Tsamardinos, L. E. Brown, and C. F. Aliferis, The max-min hill-climbing Bayesian network structure learning algorithm, Machine Learning, vol.65, pp.31-78, 2006.

J. Vandel, B. Mangin, and S. Givry, New local move operators for Bayesian network structure learning, Proceedings of PGM-12, 2012.

T. Verma and J. Pearl, Causal networks: semantics and expressiveness, UAI, pp.69-78, 1988.

S. Yaramakala and D. Margaritis, Speculative Markov blanket discovery for optimal feature selection, Fifth IEEE international conference on Data Mining, p.4, 2005.

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

L. Nevin, D. Zhang, and . Poole, A simple approach to Bayesian network computations, Proc. of the Tenth Canadian Conference on Artificial Intelligence, 1994.