X. For, find if T Ne + (X) then X is removed from CC 1 (T) It uses Ne + , because X belongs to the time slice 1>0 and the set of all parents and children of X exists in Ne +

H. Akaike, Statistical predictor identification, Annals of the Institute of Statistical Mathematics, vol.3, issue.1, pp.203-217, 1970.
DOI : 10.1007/BF02506337

S. Andreassen, F. V. Jensen, S. K. Andersen, B. Falck, U. Kjaerulff et al., MUNIN ? an expert EMG assistant, Computer-Aided Electromyography and Expert Systems, chapter 21, p.45, 1989.

A. Antonucci and M. Zaffalon, Decision-theoretic specification of credal networks: A unified language for uncertain modeling with sets of Bayesian networks, International Journal of Approximate Reasoning, vol.49, issue.2, pp.345-361, 2008.
DOI : 10.1016/j.ijar.2008.02.005

Z. Aydin, A. Singh, J. Bilmes, and W. Noble, Learning sparse models for a dynamic Bayesian network classifier of protein secondary structure, BMC Bioinformatics, vol.12, issue.1, pp.154-56, 2011.
DOI : 10.1109/TCBB.2006.17

M. Bartlett and J. Cussens, Advances in Bayesian network learning using integer programming, Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, p.46, 2013.

T. Bayes, AN ESSAY TOWARDS SOLVING A PROBLEM IN THE DOCTRINE OF CHANCES, Biometrika, vol.45, issue.3-4, pp.370-418
DOI : 10.1093/biomet/45.3-4.296

I. Beinlich, G. Suermondt, R. Chavez, and G. Cooper, The ALARM Monitoring System: A Case Study with two Probabilistic Inference Techniques for Belief Networks, Proceedings of the 2nd European Conference on AI and Medicine, pp.247-256, 1989.
DOI : 10.1007/978-3-642-93437-7_28

C. Boutilier, R. Dearden, and M. Goldszmidt, Exploiting structure in policy construction, Proceedings of the International Joint Conference on Artificial Intelligence, pp.1104-1113, 1995.

T. Charitos, L. C. Van-der-gaag, S. Visscher, K. A. Schurink, and L. P. , A dynamic Bayesian network for diagnosing ventilator-associated pneumonia in ICU patients, Expert Systems with Applications, vol.36, issue.2, pp.1249-1258, 2009.
DOI : 10.1016/j.eswa.2007.11.065

D. Chickering, Learning Bayesian networks is np-complete Learning from data: Artificial intelligence and statistics V, pp.35-66, 1996.

D. Chickering, D. Geiger, and D. E. Heckerman, Learning Bayesian networks is NP-hard, p.66, 1994.
DOI : 10.1007/978-1-4612-2404-4_12

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.21.1322

D. Chickering, C. Meek, and D. Heckerman, Large-sample learning of Bayesian networks is np-hard, Journal of Machine Learning Research, vol.5, pp.1287-1330, 2004.

D. M. Chickering, Learning equivalence classes of Bayesian-network structures, Journal of Machine Learning Research, vol.2, pp.445-498, 2002.

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://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.133.9772

C. Conati, A. S. Gertner, K. Vanlehn, and M. J. , On-Line Student Modeling for Coached Problem Solving Using Bayesian Networks, Proceedings of the Sixth International Conference on User Modeling (UM?96), pp.231-242, 1997.
DOI : 10.1007/978-3-7091-2670-7_24

G. Cooper and E. Hersovits, A Bayesian method for the induction of probabilistic networks from data, Maching Learning, pp.309-347, 1992.
DOI : 10.1007/BF00994110

D. Chickering and D. Heckerman, Efficient Approximation for the Marginal Likelihood of Incomplete Data given a Bayesian Network, UAI'96, pp.158-168, 1996.

D. Dash and M. Druzdzel, A robust independence test for constraintbased learning of causal structure, Proceedings of the Nineteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-03), pp.167-174, 2003.

C. P. De-campos, Z. Zeng, and Q. Ji, Structure learning of Bayesian networks using constraints, Proceedings of the 26th Annual International Conference on Machine Learning, ICML '09, pp.113-120, 2009.
DOI : 10.1145/1553374.1553389

M. Luis, J. G. De-campos, and . Castellano, Bayesian network learning algorithms using structural restrictions, International Journal of Approximate Reasoning, pp.233-254, 2007.

M. De-jongh and M. J. , A comparison of structural distance measures for causal Bayesian network models. Recent Advances in Intelligent Information Systems, Challenging Problems of Science, Computer Science series A model for reasoning about persistence and causation, Artificial Intelligence, vol.15, issue.21, pp.443-456, 1989.

A. Dempster, N. Laird, and D. Rubin, Maximum likelihood from incompete data via the em algorithm, Journal of the Royal Statistical Society, vol.39, pp.1-38, 1977.

T. Dietterich, Hierarchical reinforcement learning with the maxq value function decomposition, Journal of Artificial Intelligence Research, vol.13, pp.227-303, 2000.

T. G. Dietterich, Machine learning, In Nature Encyclopedia of Cognitive Science, p.20, 2003.

N. Dojer, Learning Bayesian Networks Does Not Have to Be NP-Hard, MFCS 2006, pp.305-314
DOI : 10.1007/11821069_27

R. Donat, Modélisation de la fiabilité et de la Maintenancè a partir de modèles Graphiques probabilistes, Thèse de doctorat, p.113, 2009.

A. S. Fast, Learning the structure of Bayesian networks with constraint satisfaction. Phd, p.44, 2010.

Y. Freund, H. S. Seung, E. Shamir, and N. Tishby, Selective sampling using the query by committee algorithm, Machine Learning, pp.133-168, 1997.

N. Friedman, The Bayesian structural em algorithm, Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence, pp.129-138, 1996.

N. Friedman, K. Murphy, and S. Russell, Learning the structure of dynamic probabilistic networks, Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence (UAI-98), pp.139-147, 1998.

N. Friedman, I. Nachman, and D. Peér, Learning Bayesian network structure from massive datasets: The " sparse candidate " algorithm, Proceeding UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence, pp.206-215, 1999.

A. José, J. L. Gámez, J. M. Mateo, and . Puerta, Learning Bayesian networks by hill climbing: efficient methods based on progressive restriction of the neighborhood, Data Mining and Knowledge Discovery, vol.22, issue.98, pp.106-148, 2011.

S. Gao, Q. Xiao, Q. Pan, and Q. Li, Learning dynamic Bayesian networks structure based on Bayesian optimization algorithm Advances in Neural Networks, Computer Science, vol.4492, issue.59, pp.424-431, 2007.

S. Geman and D. Geman, Stochastic relaxation gibbs distributions, and the Bayesian restoration of images, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.6, issue.6, pp.721-741, 1984.

M. F. Geyik, C. Ayaz, M. K. , C. ¨. Ust¨unust¨, and . Ust¨un, Surveillance of nosocomial infections in dicle university hospital: a ten-year experience

M. Ghavamzadeh and S. Mahadevan, Continuous-time hierarchical reinforcement learning, Proceedings of the International Conference on Machine Learning, pp.186-193, 2001.

J. A. Gomez, J. L. Mateo, and J. M. Puerta, Learning Bayesian networks by hill climbing: efficient methods based on progressive restriction of the neighborhood, Data Mining and Knowledge Discovery, vol.8, issue.4, pp.106-148, 2011.
DOI : 10.1007/s10618-010-0178-6

H. Kallel, M. Bouaziz, H. Ksibi, H. Chelly, C. B. Hmida et al., Prevalence of hospital-acquired infection in a Tunisian hospital, Journal of Hospital Infection, vol.59, issue.4, pp.343-347, 2005.
DOI : 10.1016/j.jhin.2004.09.015

D. Heckerman, A tutorial on learning with Bayesian network, Michael I

E. Jordan, Learning in Graphical Models, pp.301-354, 1998.
DOI : 10.1007/978-94-011-5014-9

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

J. W. Hwang, Y. S. Lee, and C. S. , Structure evolution of dynamic Bayesian network for traffic accident detection, 2011 IEEE Congress of Evolutionary Computation (CEC), pp.1655-1671, 2011.
DOI : 10.1109/CEC.2011.5949815

K. Hwang, B. Kim, and B. Zhang, Learning Hierarchical Bayesian Networks for Large-Scale Data Analysis, CONIP 2006, pp.670-679, 2006.
DOI : 10.1007/11893028_75

S. Jaime, F. G. Ide, and . Cozman, Random generation of Bayesian networks, In Brazilian Symp. on Artificial Intelligence, pp.366-375

J. Georges and M. Luciano, Prediction of financial time series with timeline hidden Markov experts and anns, Wseas Transactions on Business and Economics, vol.4, issue.9, pp.140-146, 2007.

J. Hulst, Modeling physiological processes with dynamic Bayesian networks, Faculty of Electrical Engineering, Mathematics and Coputer Science, p.52, 2006.

C. S. Jensen and A. Kong, Blocking Gibbs Sampling for Linkage Analysis in Large Pedigrees with Many Loops, The American Journal of Human Genetics, vol.65, issue.3, p.20, 1996.
DOI : 10.1086/302524

C. S. Jensen and A. Kong, Blocking Gibbs Sampling for Linkage Analysis in Large Pedigrees with Many Loops, The American Journal of Human Genetics, vol.65, issue.3, pp.45-97, 1996.
DOI : 10.1086/302524

A. Jonsson and A. Barto, Active Learning of Dynamic Bayesian Networks in Markov Decision Processes, LNAI, vol.4612, issue.58, pp.273-284, 2007.
DOI : 10.1007/978-3-540-73580-9_22

M. Jordan, Learning in graphical models, Academic Publishers, p.117, 1998.
DOI : 10.1007/978-94-011-5014-9

M. I. Jordan, Graphical Models, Statistical Science, vol.19, issue.1, pp.140-155, 2004.
DOI : 10.1214/088342304000000026

P. Keen and M. Scott, An organizationnal perspective. Decision Support, p.113, 1978.

U. B. Kjaerulff and A. L. Madsen, Bayesian Networks and Influence Diagrams, p.114, 2008.
DOI : 10.1007/978-0-387-74101-7

W. A. Knaus, D. P. Wagner, and J. Lynn, Short term mortality predictions for critically ill hospitalised adults, Science and ethics, vol.254, pp.389-394, 1991.

E. Kojima, S. Perrier, S. Imoto, and . Miyano, Optimal search on clustered structural constraint for learning Bayesian network structure, Journal of Machine Learning Research, vol.11, pp.285-310, 2010.

W. Lam and F. Bacchus, Using Causal Information and Local Measures to Learn Bayesian Networks, Proceedings of the 9th Conference on Uncertainty in Artificial Intelligence, pp.243-250, 1993.
DOI : 10.1016/B978-1-4832-1451-1.50034-2

S. Lauritzen, The EM algorithm for graphical association models with missing data, Computational Statistics & Data Analysis, vol.19, issue.2, pp.191-201, 1995.
DOI : 10.1016/0167-9473(93)E0056-A

S. Lauritzen and D. Speigelhalter, Local computations with probabilities on graphical structures and their application to expert systems, Royal statistical Society B, vol.50, issue.72, pp.157-224, 1988.

K. L. Lee, D. B. Pryor, F. E. Harrell, H. M. Califf, V. S. Behar et al., Predicting outcome in coronary disease statistical models versus expert clinicians, The American Journal of Medicine, vol.80, issue.4, pp.553-560, 1986.
DOI : 10.1016/0002-9343(86)90807-7

P. Leray and O. Francois, Bnt structure learning package : Documentation and experiments, p.58, 2005.

H. Lähdesmäki and I. Shmulevich, Learning the structure of dynamic Bayesian networks from??time series and??steady state measurements, Machine Learning, vol.5, issue.11, pp.185-217, 2008.
DOI : 10.1007/s10994-008-5053-y

H. Ltifi, M. B. Ayed, G. Trabelsi, and A. M. Alimi, Using Perspective Wall to Visualize Medical Data in the Intensive Care Unit, 2012 IEEE 12th International Conference on Data Mining Workshops, pp.72-78, 2012.
DOI : 10.1109/ICDMW.2012.90

H. Ltifi, G. Trabelsi, M. B. Ayed, and A. Alimi, Dynamic decision support system based on Bayesian networks -application to fight against the nosocomial infections, International Journal of Advanced Research in Artificial Intelligence, vol.1, issue.23, pp.22-29, 2012.

E. Maharaj, Pattern Recognition of Time Series using Wavelets, 15th Computational Statistics Conference of the International Association of Statistical Computing, p.58, 2002.
DOI : 10.1007/978-3-642-57489-4_76

A. J. Marcel, G. T. Babs, and J. F. Peter, Dynamic Bayesian networks as prognostic models for clinical patient management, Journal of Biomedical Informatics, vol.41, pp.515-529, 2008.

D. Margaritis, Learning Bayesian networks model structure from data, p.37, 2003.

M. Correa, C. Bielza, and J. Pamies-teixeira, Comparison of Bayesian networks and artificial neural networks for quality detection in a machining process, Expert Systems with Applications, vol.36, issue.3, pp.7270-7279, 2009.
DOI : 10.1016/j.eswa.2008.09.024

C. Meek, Causal inference and causal explanation with background knowledge, Proceedings of the Eleventh Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-95), pp.403-410, 1995.

M. B. Messaoud, SemCaDo: an approach for serendipitous causal discovery and ontology evolution, Thèse de doctorat, p.34, 2012.
URL : https://hal.archives-ouvertes.fr/tel-00716128

E. M. Mugambi, A. Hunter, G. Oatley, and L. Kennedy, Polynomial-fuzzy decision tree structures for classifying medical data. Knowledge-Based Systems, pp.81-87, 2004.

K. P. Murphy, Bayes net toolbox, technical report, p.98, 2002.

K. P. Murphy and ]. Murphy, Dynamic Bayesian Networks: Representation, Inference and Learning. Phd, University of California Software packages for graphical models, Bayesian networks, Bull.Int.Soc.Bayesian Anal, pp.66-80, 2002.

R. E. Neapolitan, Learning Bayesian Networks. Pearson Education, pp.32-35, 2004.

H. T. Nguyen, Réseaux bayésiens et apprentissage ensembliste pour l'´ etude différentielle de réseaux de régulation génétique, Thèse de doctorat, p.43, 2012.

P. Spirtes, C. Glymour, and R. Scheines, Causation, prediction, and search, pp.33-37, 1993.
DOI : 10.1007/978-1-4612-2748-9

J. Pearl, Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, pp.32-33, 1988.

L. Peelen, N. F. De-keizer, E. De-jonge, R. J. Bosman, A. Abu-hanna et al., Using hierarchical dynamic Bayesian networks to investigate dynamics of organ failure in patients in the Intensive Care Unit, Journal of Biomedical Informatics, vol.43, issue.2, pp.273-286, 2010.
DOI : 10.1016/j.jbi.2009.10.002

J. M. Pena, J. Bjorkegren, and J. Tegnér, Scalable, efficient and correct learning of Markov boundaries under the faithfulness assumption, Symbolic and Quantitative Approaches to Reasoning with Uncertainty, pp.136-147, 2005.

M. J. Pena, J. Bjorkegren, and J. Tegnér, Learning dynamic Bayesian network models via cross-validation, Pattern Recognition Letters, vol.26, issue.14, pp.2295-2308, 2005.
DOI : 10.1016/j.patrec.2005.04.005

J. C. Rajapakse and J. Zhou, Learning effective brain connectivity with dynamic Bayesian networks, NeuroImage, vol.37, issue.3, pp.749-760, 2007.
DOI : 10.1016/j.neuroimage.2007.06.003

S. A. Ramsey, S. L. Klemm, D. E. Zak, K. A. Kennedy, . Thorsson et al., Uncovering a Macrophage Transcriptional Program by Integrating Evidence from Motif Scanning and Expression Dynamics, PLoS Computational Biology, vol.35, issue.3, pp.1000021-54, 2008.
DOI : 10.1371/journal.pcbi.1000021.s035

J. Robinson and A. J. Hartemink, Learning non-stationary dynamic Bayesian networks, Journal of Machine Learning Research, vol.11, issue.54, pp.3647-3680, 2010.

S. Rodrigues-de-morais and A. Aussem, A Novel Scalable and Data Efficient Feature Subset Selection Algorithm, Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases -Part II, ECML PKDD '08, pp.298-312, 2008.
DOI : 10.1007/978-3-540-87481-2_20

URL : https://hal.archives-ouvertes.fr/hal-00383839

S. Kullback and R. A. Leibler, On Information and Sufficiency, The Annals of Mathematical Statistics, vol.22, issue.1, pp.79-86, 1951.
DOI : 10.1214/aoms/1177729694

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

M. Scott, Computer based support for decision making, Management decision systems, p.113, 1971.

Y. Shahar, Dimension of Time in Illness: An Objective View, Annals of Internal Medicine, vol.132, issue.1, pp.45-53, 2000.
DOI : 10.7326/0003-4819-132-1-200001040-00008

V. A. Smith, T. V. Yu, A. J. Smulders, E. D. Hartemink, and . Jarvis, Computational Inference of Neural Information Flow Networks, PLoS Computational Biology, vol.395, issue.11, pp.161-105, 2006.
DOI : 10.1371/journal.pcbi.0020161.sv001

L. Song, M. Kolar, and E. P. Xing, Time-varying dynamic Bayesian networks, p.60

D. Spiegelhalter and S. Lauritzen, Sequential updating of conditional probabilities on directed graphical structures. Networks, pp.579-605, 1990.

P. Spirtes, Conditional independence in directed cyclic graphical models for feedback, technical report cmu-phil-54, p.33, 1994.

N. Srebro, Maximum likelihood bounded tree-width Markov networks, Artificial Intelligence, vol.143, issue.1, pp.504-511, 2001.
DOI : 10.1016/S0004-3702(02)00360-0

A. R. Statnikov, I. Tsamardinos, and C. F. Aliferis, An algorithm for generation of large Bayesian networks, p.75, 2003.

H. Steck and T. S. Jaakkola, Unsupervised active learning in large domains, Proceedings of the Eighteenth Conference on Uncertainty in Artificial Intelligence (UAI-02), pp.469-476, 2002.

J. Stockel, E. A. Welsh, M. Liberton, R. Kunnvakkam, H. B. Aurora et al., Global transcriptomic analysis of Cyanothece 51142 reveals robust diurnal oscillation of central metabolic processes, Proceedings of the National Academy of Sciences, pp.6156-6161, 2008.
DOI : 10.1073/pnas.0711068105

J. Toepel, E. Welsh, T. C. Summerfield, H. B. Pakrasi, and L. A. Sherman, Differential Transcriptional Analysis of the Cyanobacterium Cyanothece sp. Strain ATCC 51142 during Light-Dark and Continuous-Light Growth, RNTI E19 Extraction et Gestion des Connaissances EGC, pp.3904-3913, 2008.
DOI : 10.1128/JB.00206-08

G. Trabelsi, P. Leray, M. B. Ayed, and A. M. Alimi, Benchmarking dynamic Bayesian network structure learning algorithms, 2013 5th International Conference on Modeling, Simulation and Applied Optimization (ICMSAO), pp.1-6, 2013.
DOI : 10.1109/ICMSAO.2013.6552549

URL : https://hal.archives-ouvertes.fr/hal-00812171

G. Trabelsi, P. Leray, M. B. Ayed, and A. M. Alimi, Dynamic MMHC: A Local Search Algorithm for Dynamic Bayesian Network Structure Learning, The Twelfth International Symposium on Intelligent Data Analysis, pp.392-403, 2013.
DOI : 10.1007/978-3-642-41398-8_34

URL : https://hal.archives-ouvertes.fr/hal-00864162

I. Tsamardinos, C. F. Aliferis, and A. R. Statnikov, Time and sample efficient discovery of Markov blankets and direct causal relations, Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining , KDD '03, pp.673-678, 2003.
DOI : 10.1145/956750.956838

I. Tsamardinos and G. Borboudakis, Permutation Testing Improves Bayesian Network Learning, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2010), p.96, 2010.
DOI : 10.1007/978-3-642-15939-8_21

I. Tsamardinos, L. E. Brown, C. F. Constantin, and F. Aliferis, The max-min hill-climbing Bayesian network structure learning algorithm, Machine Learning, vol.9, issue.2/3, pp.31-78, 2006.
DOI : 10.1007/s10994-006-6889-7

I. Tsamardinos, F. Constantin, and A. R. Statnikov, Algorithms for large scale Markov blanket discovery, 16th International FLAIRS Conference, pp.376-380, 2003.

I. Tsamardinos, A. R. Statnikov, L. E. Brown, and F. Constantin, Generating realistic large Bayesian networks by tiling, Proceedings of the 19th International Florida Artificial Intelligence Research Society Conference (FLAIRS), pp.592-597, 2006.

A. Tucker and X. Liu, Extending evolutionary programming methods to the learning of dynamic Bayesian networks, Proceedings of the Genetic and Evolutionary Computation Conference, pp.923-929, 1999.

A. Tucker, X. Liu, and A. Ogden-swift, Evolutionary learning of dynamic probabilistic models with large time lags, International Journal of Intelligent Systems, vol.21, issue.5, pp.621-646, 2001.
DOI : 10.1002/int.1027

T. Verma and J. Pearl, Equivalence and synthesis of causal models, Proceedings of the Sixth Conference on Uncertainty in Artificial Intelligence, pp.220-227, 1991.

N. X. Vinh, M. Chetty, R. Coppel, and P. Wangikar, Local and Global Algorithms for Learning Dynamic Bayesian Networks, 2012 IEEE 12th International Conference on Data Mining, pp.685-694, 2012.
DOI : 10.1109/ICDM.2012.18

N. Vinh, M. Chetty, R. L. Coppel, and P. P. Wangikar, Polynomial Time Algorithm for Learning Globally Optimal Dynamic Bayesian Network, ICONIP 2011, pp.719-729, 2011.
DOI : 10.1093/bioinformatics/bth463

H. Wang, K. Yu, and H. Yao, Learning Dynamic Bayesian Networks Using Evolutionary MCMC, 2006 International Conference on Computational Intelligence and Security, pp.45-50, 2006.
DOI : 10.1109/ICCIAS.2006.294088

K. Wang, J. Zhang, and F. Shen, Adaptive learning of dynamic Bayesian networks with changing structures by detecting geometric structures of time series, Knowledge and Information Systems, vol.10, issue.1, pp.121-133, 2008.
DOI : 10.1007/s10115-008-0124-8

B. Wilczynski and N. Dojer, BNFinder: exact and efficient method for learning Bayesian networks, Bioinformatics, vol.25, issue.2, pp.286-287
DOI : 10.1093/bioinformatics/btn505

M. Wynkoop and T. Dietterich, Learning MDP Action Models Via Discrete Mixture Trees, pp.597-612, 2008.
DOI : 10.1007/978-3-540-87481-2_39

J. Xu, H. Ge, X. Zhou, J. Yan, Q. Chi et al., Prediction of vascular tissue engineering results with artificial neural networks, Journal of Biomedical Informatics, vol.38, issue.6, pp.417-421, 2005.
DOI : 10.1016/j.jbi.2005.03.002