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 + ,
Statistical predictor identification, Annals of the Institute of Statistical Mathematics, vol.3, issue.1, pp.203-217, 1970. ,
DOI : 10.1007/BF02506337
MUNIN ? an expert EMG assistant, Computer-Aided Electromyography and Expert Systems, chapter 21, p.45, 1989. ,
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
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
Advances in Bayesian network learning using integer programming, Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, p.46, 2013. ,
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
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
Exploiting structure in policy construction, Proceedings of the International Joint Conference on Artificial Intelligence, pp.1104-1113, 1995. ,
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
Learning Bayesian networks is np-complete Learning from data: Artificial intelligence and statistics V, pp.35-66, 1996. ,
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
Large-sample learning of Bayesian networks is np-hard, Journal of Machine Learning Research, vol.5, pp.1287-1330, 2004. ,
Learning equivalence classes of Bayesian-network structures, Journal of Machine Learning Research, vol.2, pp.445-498, 2002. ,
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
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
A Bayesian method for the induction of probabilistic networks from data, Maching Learning, pp.309-347, 1992. ,
DOI : 10.1007/BF00994110
Efficient Approximation for the Marginal Likelihood of Incomplete Data given a Bayesian Network, UAI'96, pp.158-168, 1996. ,
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. ,
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
Bayesian network learning algorithms using structural restrictions, International Journal of Approximate Reasoning, pp.233-254, 2007. ,
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. ,
Maximum likelihood from incompete data via the em algorithm, Journal of the Royal Statistical Society, vol.39, pp.1-38, 1977. ,
Hierarchical reinforcement learning with the maxq value function decomposition, Journal of Artificial Intelligence Research, vol.13, pp.227-303, 2000. ,
Machine learning, In Nature Encyclopedia of Cognitive Science, p.20, 2003. ,
Learning Bayesian Networks Does Not Have to Be NP-Hard, MFCS 2006, pp.305-314 ,
DOI : 10.1007/11821069_27
Modélisation de la fiabilité et de la Maintenancè a partir de modèles Graphiques probabilistes, Thèse de doctorat, p.113, 2009. ,
Learning the structure of Bayesian networks with constraint satisfaction. Phd, p.44, 2010. ,
Selective sampling using the query by committee algorithm, Machine Learning, pp.133-168, 1997. ,
The Bayesian structural em algorithm, Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence, pp.129-138, 1996. ,
Learning the structure of dynamic probabilistic networks, Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence (UAI-98), pp.139-147, 1998. ,
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. ,
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. ,
Learning dynamic Bayesian networks structure based on Bayesian optimization algorithm Advances in Neural Networks, Computer Science, vol.4492, issue.59, pp.424-431, 2007. ,
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. ,
Surveillance of nosocomial infections in dicle university hospital: a ten-year experience ,
Continuous-time hierarchical reinforcement learning, Proceedings of the International Conference on Machine Learning, pp.186-193, 2001. ,
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
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
A tutorial on learning with Bayesian network, Michael I ,
Learning in Graphical Models, pp.301-354, 1998. ,
DOI : 10.1007/978-94-011-5014-9
Learning Bayesian networks: The combination of knowledge and statistical data, Machine Learning, pp.197-243, 1995. ,
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
Learning Hierarchical Bayesian Networks for Large-Scale Data Analysis, CONIP 2006, pp.670-679, 2006. ,
DOI : 10.1007/11893028_75
Random generation of Bayesian networks, In Brazilian Symp. on Artificial Intelligence, pp.366-375 ,
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. ,
Modeling physiological processes with dynamic Bayesian networks, Faculty of Electrical Engineering, Mathematics and Coputer Science, p.52, 2006. ,
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
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
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
Learning in graphical models, Academic Publishers, p.117, 1998. ,
DOI : 10.1007/978-94-011-5014-9
Graphical Models, Statistical Science, vol.19, issue.1, pp.140-155, 2004. ,
DOI : 10.1214/088342304000000026
An organizationnal perspective. Decision Support, p.113, 1978. ,
Bayesian Networks and Influence Diagrams, p.114, 2008. ,
DOI : 10.1007/978-0-387-74101-7
Short term mortality predictions for critically ill hospitalised adults, Science and ethics, vol.254, pp.389-394, 1991. ,
Optimal search on clustered structural constraint for learning Bayesian network structure, Journal of Machine Learning Research, vol.11, pp.285-310, 2010. ,
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
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
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. ,
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
Bnt structure learning package : Documentation and experiments, p.58, 2005. ,
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
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
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. ,
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
Dynamic Bayesian networks as prognostic models for clinical patient management, Journal of Biomedical Informatics, vol.41, pp.515-529, 2008. ,
Learning Bayesian networks model structure from data, p.37, 2003. ,
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
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. ,
SemCaDo: an approach for serendipitous causal discovery and ontology evolution, Thèse de doctorat, p.34, 2012. ,
URL : https://hal.archives-ouvertes.fr/tel-00716128
Polynomial-fuzzy decision tree structures for classifying medical data. Knowledge-Based Systems, pp.81-87, 2004. ,
Bayes net toolbox, technical report, p.98, 2002. ,
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. ,
Learning Bayesian Networks. Pearson Education, pp.32-35, 2004. ,
Réseaux bayésiens et apprentissage ensembliste pour l'´ etude différentielle de réseaux de régulation génétique, Thèse de doctorat, p.43, 2012. ,
Causation, prediction, and search, pp.33-37, 1993. ,
DOI : 10.1007/978-1-4612-2748-9
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, pp.32-33, 1988. ,
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
Scalable, efficient and correct learning of Markov boundaries under the faithfulness assumption, Symbolic and Quantitative Approaches to Reasoning with Uncertainty, pp.136-147, 2005. ,
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
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
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
Learning non-stationary dynamic Bayesian networks, Journal of Machine Learning Research, vol.11, issue.54, pp.3647-3680, 2010. ,
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
On Information and Sufficiency, The Annals of Mathematical Statistics, vol.22, issue.1, pp.79-86, 1951. ,
DOI : 10.1214/aoms/1177729694
Estimating the Dimension of a Model, The Annals of Statistics, vol.6, issue.2, pp.461-464, 1978. ,
DOI : 10.1214/aos/1176344136
Computer based support for decision making, Management decision systems, p.113, 1971. ,
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
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
Time-varying dynamic Bayesian networks, p.60 ,
Sequential updating of conditional probabilities on directed graphical structures. Networks, pp.579-605, 1990. ,
Conditional independence in directed cyclic graphical models for feedback, technical report cmu-phil-54, p.33, 1994. ,
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
An algorithm for generation of large Bayesian networks, p.75, 2003. ,
Unsupervised active learning in large domains, Proceedings of the Eighteenth Conference on Uncertainty in Artificial Intelligence (UAI-02), pp.469-476, 2002. ,
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
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
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
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
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
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
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
Algorithms for large scale Markov blanket discovery, 16th International FLAIRS Conference, pp.376-380, 2003. ,
Generating realistic large Bayesian networks by tiling, Proceedings of the 19th International Florida Artificial Intelligence Research Society Conference (FLAIRS), pp.592-597, 2006. ,
Extending evolutionary programming methods to the learning of dynamic Bayesian networks, Proceedings of the Genetic and Evolutionary Computation Conference, pp.923-929, 1999. ,
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
Equivalence and synthesis of causal models, Proceedings of the Sixth Conference on Uncertainty in Artificial Intelligence, pp.220-227, 1991. ,
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
Polynomial Time Algorithm for Learning Globally Optimal Dynamic Bayesian Network, ICONIP 2011, pp.719-729, 2011. ,
DOI : 10.1093/bioinformatics/bth463
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
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
BNFinder: exact and efficient method for learning Bayesian networks, Bioinformatics, vol.25, issue.2, pp.286-287 ,
DOI : 10.1093/bioinformatics/btn505
Learning MDP Action Models Via Discrete Mixture Trees, pp.597-612, 2008. ,
DOI : 10.1007/978-3-540-87481-2_39
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