{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T06:55:43Z","timestamp":1772780143617,"version":"3.50.1"},"reference-count":477,"publisher":"Maximum Academic Press","issue":"2","license":[{"start":{"date-parts":[[2011,5,12]],"date-time":"2011-05-12T00:00:00Z","timestamp":1305158400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/www.cambridge.org\/core\/terms"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["The Knowledge Engineering Review"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Bayesian networks have become a widely used method in the modelling of uncertain knowledge. Owing to the difficulty domain experts have in specifying them, techniques that learn Bayesian networks from data have become indispensable. Recently, however, there have been many important new developments in this field. This work takes a broad look at the literature on learning Bayesian networks\u2014in particular their structure\u2014from data. Specific topics are not focused on in detail, but it is hoped that all the major fields in the area are covered. This article is not intended to be a tutorial\u2014for this, there are many books on the topic, which will be presented. However, an effort has been made to locate all the relevant publications, so that this paper can be used as a ready reference to find the works on particular sub-topics.<\/jats:p>","DOI":"10.1017\/s0269888910000251","type":"journal-article","created":{"date-parts":[[2011,5,12]],"date-time":"2011-05-12T07:35:36Z","timestamp":1305185736000},"page":"99-157","source":"Crossref","is-referenced-by-count":255,"title":["Learning Bayesian networks: approaches and issues"],"prefix":"10.48130","volume":"26","author":[{"given":"R\u00f3n\u00e1n","family":"Daly","sequence":"first","affiliation":[]},{"given":"Qiang","family":"Shen","sequence":"additional","affiliation":[]},{"given":"Stuart","family":"Aitken","sequence":"additional","affiliation":[]}],"member":"27968","published-online":{"date-parts":[[2011,5,12]]},"reference":[{"key":"S0269888910000251_ref421","first-page":"356","article-title":"Learning Bayesian belief networks based on the MDL principle: an efficient algorithm using the branch and bound technique","volume":"E82","author":"Suzuki","year":"1999","journal-title":"IEICE Transactions on Information and Systems"},{"key":"S0269888910000251_ref417","unstructured":"Suermondt H. J. , Cooper G. F. 1988. Updating Probabilities in Multiply-Connected Belief Networks. Technical report SMI-88-0207, Medical Computer Science Group, Stanford University."},{"key":"S0269888910000251_ref411","first-page":"697","volume-title":"Advances in Neural Information Processing Systems 15 (NIPS*2002)","author":"Steck","year":"2003a"},{"key":"S0269888910000251_ref460","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2002.1183994"},{"key":"S0269888910000251_ref440","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-30217-9_15"},{"key":"S0269888910000251_ref428","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2003.1250936"},{"key":"S0269888910000251_ref451","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-008-5099-x"},{"key":"S0269888910000251_ref385","doi-asserted-by":"publisher","DOI":"10.1016\/S0888-613X(97)89135-9"},{"key":"S0269888910000251_ref477","first-page":"560","volume-title":"Proceedings of the Twenty-second Annual Conference on Uncertainty in Artificial Intelligence (UAI-06)","author":"Zuk","year":"2006"},{"key":"S0269888910000251_ref476","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipl.2008.03.015"},{"key":"S0269888910000251_ref473","unstructured":"Zhang N. L. , Poole D. 1994b. A simple approach to Bayesian network computations. In Proceedings of the Tenth Biennial Conference of the Canadian Society for Computational Studies of Intelligence, Banff, Canada, 171\u2013178."},{"key":"S0269888910000251_ref471","doi-asserted-by":"publisher","DOI":"10.1016\/S0004-3702(96)00035-5"},{"key":"S0269888910000251_ref469","first-page":"1437","article-title":"Causal reasoning with ancestral graphs","volume":"9","author":"Zhang","year":"2008","journal-title":"Journal of Machine Learning Research"},{"key":"S0269888910000251_ref459","doi-asserted-by":"publisher","DOI":"10.1109\/34.748825"},{"key":"S0269888910000251_ref423","first-page":"306","volume-title":"Proceedings of the First International Conference on Knowledge Discovery and Data Mining (KDD-95)","author":"Thiesson","year":"1995"},{"key":"S0269888910000251_ref470","doi-asserted-by":"publisher","DOI":"10.1007\/s11023-008-9096-4"},{"key":"S0269888910000251_ref454","volume-title":"Graphical Models in Applied Multivariate Statistics","author":"Whittaker","year":"1990"},{"key":"S0269888910000251_ref452","doi-asserted-by":"publisher","DOI":"10.1162\/089976600300015880"},{"key":"S0269888910000251_ref436","first-page":"100","volume-title":"Advances in Intelligent Data Analysis V: Proceedings of the Fifth International Symposium on Intelligent Data Analysis (IDA 2003)","author":"Tucker","year":"2004"},{"key":"S0269888910000251_ref420","doi-asserted-by":"publisher","DOI":"10.1016\/B978-1-4832-1451-1.50037-8"},{"key":"S0269888910000251_ref402","first-page":"294","volume-title":"Proceedings of First International Conference on Knowledge Discovery and Data Mining","author":"Spirtes","year":"1995"},{"key":"S0269888910000251_ref443","doi-asserted-by":"publisher","DOI":"10.1109\/34.608295"},{"key":"S0269888910000251_ref437","first-page":"923","volume-title":"Proceedings of the Genetic and Evolutionary Computation Conference","author":"Tucker","year":"1999"},{"key":"S0269888910000251_ref393","doi-asserted-by":"publisher","DOI":"10.1016\/B978-1-4832-1451-1.50036-6"},{"key":"S0269888910000251_ref446","doi-asserted-by":"publisher","DOI":"10.1093\/comjnl\/11.2.185"},{"key":"S0269888910000251_ref435","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-006-6889-7"},{"key":"S0269888910000251_ref433","doi-asserted-by":"publisher","DOI":"10.1145\/956750.956838"},{"key":"S0269888910000251_ref401","doi-asserted-by":"publisher","DOI":"10.1177\/089443939100900106"},{"key":"S0269888910000251_ref445","doi-asserted-by":"publisher","DOI":"10.1016\/B978-1-4832-8287-9.50049-9"},{"key":"S0269888910000251_ref475","doi-asserted-by":"publisher","DOI":"10.1016\/S0888-613X(98)10014-2"},{"key":"S0269888910000251_ref472","first-page":"606","volume-title":"Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence (UAI-94)","author":"Zhang","year":"1994a"},{"key":"S0269888910000251_ref413","unstructured":"Steck H. , Jaakkola T. S. 2003b. (Semi-)predictive discretization during model selection. AI Memo 2003-002, Artificial Intelligence Laboratory, Massachusetts Institute of Technology."},{"key":"S0269888910000251_ref388","doi-asserted-by":"publisher","DOI":"10.1016\/0010-4809(91)90020-W"},{"key":"S0269888910000251_ref419","doi-asserted-by":"publisher","DOI":"10.1016\/0004-3702(91)90091-W"},{"key":"S0269888910000251_ref408","doi-asserted-by":"publisher","DOI":"10.1016\/B978-1-4832-1451-1.50030-5"},{"key":"S0269888910000251_ref425","unstructured":"Thiesson B. , Meek C. , Chickering D. M. , Heckerman D. 1998a. Learning Mixtures of Bayesian Networks. Technical report MSR-TR-97-30, Microsoft Research."},{"key":"S0269888910000251_ref416","doi-asserted-by":"publisher","DOI":"10.1007\/s00500-002-0223-5"},{"key":"S0269888910000251_ref221","first-page":"325","volume-title":"Proceedings of the Twelfth Conference on Uncertainty in Artificial Intelligence (UAI-96)","author":"Huang","year":"1996"},{"key":"S0269888910000251_ref135","first-page":"305","volume-title":"Proceedings of the Thirty-First International Symposium on Mathematical Foundations of Computer Science","author":"Dojer","year":"2006"},{"key":"S0269888910000251_ref108","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511811357"},{"key":"S0269888910000251_ref368","first-page":"1146","volume-title":"Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence (IJCAI 95)","author":"Russell","year":"1995"},{"key":"S0269888910000251_ref410","first-page":"511","volume-title":"Proceedings of the Twenty-fourth Conference on Uncertainty in Artificial Intelligence (UAI-08)","author":"Steck","year":"2008"},{"key":"S0269888910000251_ref354","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1613\/jair.1122","article-title":"Exploiting contextual independence in probabilistic inference","volume":"18","author":"Poole","year":"2003","journal-title":"Journal of Artificial Intelligence Research"},{"key":"S0269888910000251_ref149","doi-asserted-by":"publisher","DOI":"10.1109\/CIDM.2007.368847"},{"key":"S0269888910000251_ref158","first-page":"252","volume-title":"Proceedings of the Twelfth Conference on Uncertainty in Artificial Intelligence (UAI-96)","author":"Friedman","year":"1996b"},{"key":"S0269888910000251_ref139","first-page":"43","article-title":"Qualitative verbal explanations in Bayesian belief networks","volume":"94","author":"Druzdzel","year":"1996","journal-title":"Artificial Intelligence and Simulation of Behaviour Quarterly"},{"key":"S0269888910000251_ref246","doi-asserted-by":"publisher","DOI":"10.1109\/ICSMC.1997.637339"},{"key":"S0269888910000251_ref72","first-page":"445","article-title":"Learning equivalence classes of Bayesian-network structures","volume":"2","author":"Chickering","year":"2002","journal-title":"Journal of Machine Learning Research"},{"key":"S0269888910000251_ref259","first-page":"241","volume-title":"Proceedings of the Twenty-second Annual Conference on Uncertainty in Artificial Intelligence (UAI-06)","author":"Koivisto","year":"2006"},{"key":"S0269888910000251_ref456","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2006.56"},{"key":"S0269888910000251_ref457","doi-asserted-by":"publisher","DOI":"10.1016\/j.dss.2008.01.002"},{"key":"S0269888910000251_ref94","doi-asserted-by":"publisher","DOI":"10.1016\/0933-3657(93)90020-4"},{"key":"S0269888910000251_ref37","doi-asserted-by":"publisher","DOI":"10.1016\/0888-613X(95)00114-V"},{"key":"S0269888910000251_ref232","first-page":"360","volume-title":"Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence (UAI-94)","author":"Jensen","year":"1994"},{"key":"S0269888910000251_ref287","unstructured":"Lucas P. 2002. Restricted Bayesian network structure learning. In Proceedings of the First European Workshop on Probabilistic Graphical Models (PGM 2002), G\u00e1mez J. A. & Salmeron A. (eds). 117\u2013126."},{"key":"S0269888910000251_ref125","first-page":"251","article-title":"Learning Bayesian networks by ant colony optimisation: searching in two different spaces","volume":"9","author":"de Campos","year":"2002c","journal-title":"Mathware and Soft Computing"},{"key":"S0269888910000251_ref466","first-page":"689","volume-title":"Advances in Neural Information Processing Systems 13 (NIPS*2000)","author":"Yedidia","year":"2001"},{"key":"S0269888910000251_ref79","unstructured":"Chickering D. M. , Meek C. 2002. Finding optimal Bayesian networks. In Proceedings of the Eighteenth Conference on Uncertainty in Artificial Intelligence (UAI-02), Darwiche, A. & Friedman, N. (eds). Morgan Kaufmann, 94\u2013102."},{"key":"S0269888910000251_ref194","first-page":"198","volume-title":"Proceedings of the Thirteenth Conference on Uncertainty in Artificial Intelligence (UAI-97)","author":"Greiner","year":"1997"},{"key":"S0269888910000251_ref160","first-page":"201","volume-title":"Proceedings of the Sixteenth Conference on Uncertainty in Artificial Intelligence (UAI-00)","author":"Friedman","year":"2000"},{"key":"S0269888910000251_ref156","volume-title":"Proceedings of the Seventh International Workshop on Artificial Intelligence and Statistics","author":"Friedman","year":"1999"},{"key":"S0269888910000251_ref274","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1111\/j.2517-6161.1988.tb01721.x","article-title":"Local computations with probabilities on graphical structures and their application to expert systems","volume":"50","author":"Lauritzen","year":"1988","journal-title":"Journal of the Royal Statistical Society. Series B (Methodological)"},{"key":"S0269888910000251_ref81","unstructured":"Chickering D. M. , Geiger D. , Heckerman D. 1996. Learning Bayesian networks: search methods and experimental results. In Learning from Data: Artificial Intelligence and Statistics V, Fisher, D. & Lenz, H.-J. (eds). Lecture Notes in Statistics 112, 112\u2013128. Springer."},{"key":"S0269888910000251_ref59","doi-asserted-by":"publisher","DOI":"10.1002\/(SICI)1097-0037(199608)28:1<31::AID-NET5>3.0.CO;2-E"},{"key":"S0269888910000251_ref78","unstructured":"Chickering D. M. , Heckerman D. 1999. Fast learning from sparse data. In Proceedings of the Fifteenth Annual Conference on Uncertainty in Artificial Intelligence (UAI-99), Morgan Kaufmann, 109\u2013115."},{"key":"S0269888910000251_ref1","doi-asserted-by":"publisher","DOI":"10.1016\/0169-2070(91)90004-F"},{"key":"S0269888910000251_ref362","first-page":"175","volume-title":"Uncertainty in Artificial Intelligence 3","author":"Rebane","year":"1987"},{"key":"S0269888910000251_ref377","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-72927-3_15"},{"key":"S0269888910000251_ref68","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1613\/jair.764","article-title":"AIS-BN: an adaptive importance sampling algorithm for evidential reasoning in large Bayesian networks","volume":"13","author":"Cheng","year":"2000","journal-title":"Journal of Artificial Intelligence Research"},{"key":"S0269888910000251_ref170","doi-asserted-by":"publisher","DOI":"10.1016\/B978-0-444-88738-2.50023-3"},{"key":"S0269888910000251_ref447","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-58648-4_7"},{"key":"S0269888910000251_ref320","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-30217-9_62"},{"key":"S0269888910000251_ref56","unstructured":"Castelo R. , Perlman M. D. 2002. Learning essential graph Markov models from data. In Proceedings of the First European Workshop on Probabilistic Graphical Models (PGM 2002), G\u00e1mez, J. A. & Salmer\u00f3n, A. (eds). Cuenca, Spain, 17\u201324."},{"key":"S0269888910000251_ref83","unstructured":"Chickering D. M. , Heckerman D. , Meek C. 1997b. A Bayesian Approach to Learning Bayesian Networks with Local Structure. Technical report MSR-TR-97-07, Microsoft Research."},{"key":"S0269888910000251_ref164","first-page":"139","volume-title":"Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence (UAI-98)","author":"Friedman","year":"1998"},{"key":"S0269888910000251_ref182","doi-asserted-by":"publisher","DOI":"10.1023\/A:1007425814087"},{"key":"S0269888910000251_ref414","doi-asserted-by":"publisher","DOI":"10.1093\/phisci\/axi101"},{"key":"S0269888910000251_ref42","first-page":"301","article-title":"Improving the reliability of causal discovery from small data sets using argumentation","volume":"10","author":"Bromberg","year":"2009","journal-title":"Journal of Machine Learning Research"},{"key":"S0269888910000251_ref181","doi-asserted-by":"publisher","DOI":"10.1007\/BFb0053999"},{"key":"S0269888910000251_ref180","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1214\/aos\/1009210550","article-title":"Stratified exponential families: graphical models and model selection","volume":"29","author":"Geiger","year":"2001","journal-title":"The Annals of Statistics"},{"key":"S0269888910000251_ref150","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-74825-0_5"},{"key":"S0269888910000251_ref155","first-page":"129","volume-title":"Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence (UAI-98)","author":"Friedman","year":"1998"},{"key":"S0269888910000251_ref241","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-007-5041-7"},{"key":"S0269888910000251_ref306","doi-asserted-by":"publisher","DOI":"10.1023\/A:1011096320004"},{"key":"S0269888910000251_ref422","first-page":"584","volume-title":"Proceedings of the Twenty-first Conference on Uncertainty in Artificial Intelligence (UAI-05)","author":"Teyssier","year":"2005"},{"key":"S0269888910000251_ref128","first-page":"496","volume-title":"Proceedings of the Sixth Mexican International Conference on Artificial Intelligence (MICAI 2007)","author":"de Santana","year":"2007b"},{"key":"S0269888910000251_ref474","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1613\/jair.305","article-title":"Exploiting causal independence in Bayesian network inference","volume":"5","author":"Zhang","year":"1996","journal-title":"Journal of Artificial Intelligence Research"},{"key":"S0269888910000251_ref58","doi-asserted-by":"crossref","unstructured":"Castillo E. , Guti\u00e9rrez J. M. , Hadi A. S. 1995. Parametric structure of probabilities in Bayesian networks. In Proceedings of the European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty (ECSQARU \u201995), Lecture Notes in Artificial Intelligence 946, 89\u201398. Springer.","DOI":"10.1007\/3-540-60112-0_11"},{"key":"S0269888910000251_ref298","first-page":"505","volume-title":"Advances in Neural Information Processing Systems 12 (NIPS*1999)","author":"Margaritis","year":"2000"},{"key":"S0269888910000251_ref88","doi-asserted-by":"publisher","DOI":"10.1023\/A:1009787925236"},{"key":"S0269888910000251_ref146","first-page":"479","volume-title":"Advances in Neural Information Processing Systems 13","author":"Elidan","year":"2001"},{"key":"S0269888910000251_ref203","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511611308.012"},{"key":"S0269888910000251_ref409","first-page":"558","volume-title":"Proceedings of the Sixteenth Conference on Uncertainty in Artificial Intelligence (UAI-00)","author":"Steck","year":"2000"},{"key":"S0269888910000251_ref75","doi-asserted-by":"crossref","unstructured":"Chickering D. M. 1996a. Learning Bayesian networks is NP-complete. In Learning from Data: Artificial Intelligence and Statistics V, Fisher, D. & Lenz, H.-J. (eds). Lecture Notes in Statistics 112, 121\u2013130. Springer.","DOI":"10.1007\/978-1-4612-2404-4_12"},{"key":"S0269888910000251_ref318","unstructured":"Murphy K. P. , Mian S. 1999. Modelling Gene Expression Data Using Dynamic Bayesian Networks. Technical report, Computer Science Division, University of California, Berkeley."},{"key":"S0269888910000251_ref178","first-page":"770","volume-title":"Proceedings of the Eighth National Conference on Artificial Intelligence (AAAI 1990)","author":"Geiger","year":"1990"},{"key":"S0269888910000251_ref51","doi-asserted-by":"publisher","DOI":"10.1016\/0888-613X(96)00013-8"},{"key":"S0269888910000251_ref172","doi-asserted-by":"publisher","DOI":"10.1016\/S0167-8655(01)00123-4"},{"key":"S0269888910000251_ref171","first-page":"762","volume-title":"Proceedings of the Eighth National Conference on Artificial Intelligence","author":"Fung","year":"1990"},{"key":"S0269888910000251_ref132","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-8640.2007.00320.x"},{"key":"S0269888910000251_ref99","doi-asserted-by":"publisher","DOI":"10.1016\/0004-3702(93)90036-B"},{"key":"S0269888910000251_ref93","unstructured":"Cotta C. , Muruz\u00e1bal J. 2004. On the learning of Bayesian network graph structures via evolutionary programming. In Proceedings of the Second European Workshop on Probabilistic Graphical Models, Lucas, P. (ed.). Leiden, Netherlands, 65\u201372."},{"key":"S0269888910000251_ref8","unstructured":"Acid S. , de Campos L. M. 1996b. An Algorithm for Finding Minimum d-Separating Sets in Belief Networks. Technical report DECSAI-96-02-14, Departamento de Ciencias de la Computaci\u00f3n e Inteligencia Artificial, Universidad de Granada."},{"key":"S0269888910000251_ref329","first-page":"435","volume-title":"Proceedings of the Ninteenth Conference on Uncertainty in Artificial Intelligence","author":"Nielsen","year":"2003"},{"key":"S0269888910000251_ref165","first-page":"197","volume-title":"Proceedings of the Seventh International Workshop on Artificial Intelligence and Statistics","author":"Friedman","year":"1999a"},{"key":"S0269888910000251_ref188","volume-title":"Computation, Causation, and Discovery","author":"Glymour","year":"1999"},{"key":"S0269888910000251_ref153","doi-asserted-by":"publisher","DOI":"10.1126\/science.1094068"},{"key":"S0269888910000251_ref82","unstructured":"Chickering D. M. , Heckerman D. , Meek C. 1997a. A Bayesian approach to learning Bayesian networks with local structure. In Proceedings of the Thirteenth Annual Conference on Uncertainty in Artificial Intelligence (UAI-97). Morgan Kaufmann, 80\u201389."},{"key":"S0269888910000251_ref263","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.1969.1054336"},{"key":"S0269888910000251_ref121","doi-asserted-by":"publisher","DOI":"10.1002\/(SICI)1098-111X(199707)12:7<495::AID-INT2>3.0.CO;2-G"},{"key":"S0269888910000251_ref330","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijar.2008.02.007"},{"key":"S0269888910000251_ref133","doi-asserted-by":"publisher","DOI":"10.1016\/0004-3702(95)00118-2"},{"key":"S0269888910000251_ref142","unstructured":"Eaton D. , Murphy K. 2007b. Exact Bayesian structure learning from uncertain interventions. In Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics 2, Journal of Machine Learning Research: Workshop and Conference Proceedings, Meila, M. & Shen, X. (eds). JMLR, 107\u2013114."},{"key":"S0269888910000251_ref145","first-page":"2699","article-title":"Learning bounded treewidth Bayesian networks","volume":"9","author":"Elidan","year":"2008","journal-title":"Journal of Machine Learning Research"},{"key":"S0269888910000251_ref140","doi-asserted-by":"publisher","DOI":"10.1016\/B978-1-4832-1451-1.50005-6"},{"key":"S0269888910000251_ref74","unstructured":"Chickering D. M. 1995. A transformational characterization of equivalent Bayesian network structures. In Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence (UAI-95), Besnard, P. & Hanks, S. (eds). Morgan Kaufmann, 87\u201398."},{"key":"S0269888910000251_ref295","doi-asserted-by":"publisher","DOI":"10.1109\/21.120082"},{"key":"S0269888910000251_ref97","doi-asserted-by":"crossref","unstructured":"Cruz-Ram\u00edrez N. , Acosta-Mesa H.-G. , Barrientos-Martnez R.-E. , Nava-Fern\u00e1ndez L.-A. 2006. How good are the Bayesian information criterion and the minimum description length principle for selection? A Bayesian network analysis. In Proceedings of the Fifth Mexican International Conference on Artificial Intelligence (MICAI 2006), Lecture Notes in Artificial Intelligence 4293, 494\u2013504. Springer.","DOI":"10.1007\/11925231_46"},{"key":"S0269888910000251_ref357","first-page":"450","volume-title":"Learning from Data: Artificial Intelligence and Statistics V","author":"Provan","year":"1996"},{"key":"S0269888910000251_ref151","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-75256-1_50"},{"key":"S0269888910000251_ref66","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2007.190732"},{"key":"S0269888910000251_ref157","first-page":"157","volume-title":"Proceedings of the Thirteenth International Conference on Machine Learning (ICML '96)","author":"Friedman","year":"1996a"},{"key":"S0269888910000251_ref141","unstructured":"Eaton D. , Murphy K. 2007a. Bayesian structure learning using dynamic programming and MCMC. In Proceedings of the Twenty-third Annual Conference on Uncertainty in Artificial Intelligence (UAI-07), Parr, R. & van der Gaag, L. (eds). AUAI Press, 101\u2013108."},{"key":"S0269888910000251_ref119","unstructured":"de Campos L. M. , Huete J. F. 2000a. Approximating causal orderings for Bayesian networks using genetic algorithms and simulated annealing. In Proceedings of the Eight Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, Madrid, Spain, 333\u2013340."},{"key":"S0269888910000251_ref53","doi-asserted-by":"publisher","DOI":"10.1093\/bjps\/53.3.411"},{"key":"S0269888910000251_ref322","first-page":"476","volume-title":"Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence (UAI-99)","author":"Myers","year":"1999b"},{"key":"S0269888910000251_ref95","unstructured":"Cowell R. 2001. Conditions under which conditional independence and scoring methods lead to identical selection of Bayesian network models. In Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence (UAI-01), Breese, J. & Koller, D. (eds). Morgan Kaufmann, 91\u201397."},{"key":"S0269888910000251_ref127","doi-asserted-by":"publisher","DOI":"10.1016\/j.datak.2006.10.005"},{"key":"S0269888910000251_ref289","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1994.10476894"},{"key":"S0269888910000251_ref23","doi-asserted-by":"publisher","DOI":"10.1016\/0004-3702(95)00004-6"},{"key":"S0269888910000251_ref14","doi-asserted-by":"publisher","DOI":"10.1016\/j.artint.2007.09.004"},{"key":"S0269888910000251_ref277","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2002.1183913"},{"key":"S0269888910000251_ref167","first-page":"206","volume-title":"Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence (UAI-99)","author":"Friedman","year":"1999c"},{"key":"S0269888910000251_ref379","first-page":"514","volume-title":"Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence (UAI-94)","author":"Shachter","year":"1994"},{"key":"S0269888910000251_ref2","doi-asserted-by":"publisher","DOI":"10.1016\/0169-2070(95)00664-8"},{"key":"S0269888910000251_ref262","doi-asserted-by":"publisher","DOI":"10.1007\/s11023-006-9040-4"},{"key":"S0269888910000251_ref351","doi-asserted-by":"publisher","DOI":"10.1016\/S0004-3702(96)00022-7"},{"key":"S0269888910000251_ref223","doi-asserted-by":"publisher","DOI":"10.1007\/BFb0028199"},{"key":"S0269888910000251_ref376","doi-asserted-by":"publisher","DOI":"10.1016\/S0004-3702(98)00047-2"},{"key":"S0269888910000251_ref35","doi-asserted-by":"crossref","unstructured":"Bouckaert R. R. 1994b. Properties of Measures for Bayesian Belief Network Learning. Technical report UU-CS-1994-35, Department of Information and Computing Sciences, Utrecht University.","DOI":"10.1016\/B978-1-55860-332-5.50018-3"},{"key":"S0269888910000251_ref338","volume-title":"Causality","author":"Pearl","year":"2000"},{"key":"S0269888910000251_ref103","unstructured":"Daly R. , Shen Q , Aitken S. 2006. Speeding up the learning of equivalence classes of Bayesian network structures. In Proceedings of the Tenth IASTED International Conference on Artificial Intelligence and Soft Computing, del Pobil, A. P. (ed.). ACTA Press, 34\u201339."},{"key":"S0269888910000251_ref163","doi-asserted-by":"publisher","DOI":"10.1023\/A:1007465528199"},{"key":"S0269888910000251_ref386","first-page":"575","volume-title":"Readings in Uncertain Reasoning","author":"Shenoy","year":"1990"},{"key":"S0269888910000251_ref168","doi-asserted-by":"publisher","DOI":"10.1089\/106652700750050961"},{"key":"S0269888910000251_ref363","doi-asserted-by":"crossref","first-page":"962","DOI":"10.1214\/aos\/1031689015","article-title":"Ancestral graph Markov models","volume":"30","author":"Richardson","year":"2002","journal-title":"The Annals of Statistics"},{"key":"S0269888910000251_ref67","unstructured":"Cheng J. , Druzdzel M. 2001. Confidence inference in Bayesian networks. In Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence (UAI-01), Breese, J. & Koller, D. (eds). Morgan Kaufmann, 75\u201382."},{"key":"S0269888910000251_ref49","doi-asserted-by":"crossref","unstructured":"Burge J. , Lane T. 2007. Shrinkage estimator for Bayesian network parameters. In Proceedings of the Eighteenth European Conference on Machine Learning (EMCL 2007), Kok, J. N., Koronacki, J., de Mantaras, R. L., Matwin, S., Mladeni\u010d, D. & Skowron, A. (eds). Lecture Notes in Artificial Intelligence 4701, 67\u201378. Springer.","DOI":"10.1007\/978-3-540-74958-5_10"},{"key":"S0269888910000251_ref76","unstructured":"Chickering D. M. 1996b. Learning equivalence classes of Bayesian network structures. In Proceedings of the Twelfth Conference on Uncertainty in Artificial Intelligence (UAI-96), Horvitz, E. & Jensen, F. (eds). Morgan Kaufmann, 150\u2013157."},{"key":"S0269888910000251_ref464","doi-asserted-by":"publisher","DOI":"10.1109\/FOCI.2007.372146"},{"key":"S0269888910000251_ref113","first-page":"1177","article-title":"Model averaging for prediction with discrete Bayesian networks","volume":"5","author":"Dash","year":"2004","journal-title":"Journal of Machine Learning Research"},{"key":"S0269888910000251_ref26","doi-asserted-by":"publisher","DOI":"10.1023\/A:1007421730016"},{"key":"S0269888910000251_ref266","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-008-5053-y"},{"key":"S0269888910000251_ref161","doi-asserted-by":"publisher","DOI":"10.1023\/A:1020249912095"},{"key":"S0269888910000251_ref123","doi-asserted-by":"publisher","DOI":"10.1016\/S0888-613X(02)00091-9"},{"key":"S0269888910000251_ref143","first-page":"178","volume-title":"Proceedings of the Twenty-first Conference on Uncertainty in Artificial Intelligence (UAI-05)","author":"Eberhardt","year":"2005"},{"key":"S0269888910000251_ref3","doi-asserted-by":"crossref","unstructured":"Acid S. , De Campos L. M. 2000. Learning right sized belief networks by means of a hybrid methodology. In Principles of Data Mining and Knowledge Discovery: 4th European Conference, PKDD 2000, Zighed, D. Komorowski, J. & \u017bytkow, J. (eds). Lecture Notes in Artificial Intelligence 1910, 309\u2013315, Springer.","DOI":"10.1007\/3-540-45372-5_30"},{"key":"S0269888910000251_ref47","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1613\/jair.62","article-title":"Operations for learning with graphical models","volume":"2","author":"Buntine","year":"1994","journal-title":"Journal of Artificial Intelligence Research"},{"key":"S0269888910000251_ref201","doi-asserted-by":"publisher","DOI":"10.1093\/bjps\/50.4.521"},{"key":"S0269888910000251_ref84","first-page":"1287","article-title":"Large-sample learning of Bayesian networks is NP-hard","volume":"5","author":"Chickering","year":"2004","journal-title":"Journal of Machine Learning Research"},{"key":"S0269888910000251_ref235","doi-asserted-by":"publisher","DOI":"10.1002\/net.3230200509"},{"key":"S0269888910000251_ref438","doi-asserted-by":"publisher","DOI":"10.1002\/int.1027"},{"key":"S0269888910000251_ref100","doi-asserted-by":"publisher","DOI":"10.1016\/S0004-3702(97)00013-1"},{"key":"S0269888910000251_ref202","first-page":"2523","article-title":"Active learning of causal networks with intervention experiments and optimal designs","volume":"9","author":"He","year":"2008","journal-title":"Journal of Machine Learning Research"},{"key":"S0269888910000251_ref6","doi-asserted-by":"crossref","unstructured":"Acid S. , de Campos L. M. 1995. Approximations of causal networks by polytrees: an empirical study. In Advances in Intelligent Computing \u2013 IPMU \u201994, Lecture Notes in Computer Science 945, 149\u2013158. Springer.","DOI":"10.1007\/BFb0035946"},{"key":"S0269888910000251_ref96","volume-title":"Probabilistic Networks and Expert Systems. Statistics for Engineering and Information Science","author":"Cowell","year":"1999"},{"key":"S0269888910000251_ref30","doi-asserted-by":"crossref","unstructured":"Borchani H. , Chaouachi M. , Amor N. B. 2007. Learning causal Bayesian networks from incomplete observational data and interventions. In Proceedings of the Ninth European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU 2007), Mellouli, K. (ed.). Lecture Notes in Artificial Intelligence 4724, 17\u201329, Springer.","DOI":"10.1007\/978-3-540-75256-1_5"},{"key":"S0269888910000251_ref191","doi-asserted-by":"publisher","DOI":"10.1016\/S0019-9958(67)91165-5"},{"key":"S0269888910000251_ref324","doi-asserted-by":"publisher","DOI":"10.1016\/0004-3702(92)90065-6"},{"key":"S0269888910000251_ref215","first-page":"382","article-title":"Bayesian model averaging: a tutorial","volume":"14","author":"Hoeting","year":"1999","journal-title":"Statistical Science"},{"key":"S0269888910000251_ref54","doi-asserted-by":"publisher","DOI":"10.1093\/bjps\/axi156"},{"key":"S0269888910000251_ref114","unstructured":"Dash D. , Druzdzel M. 2003. A robust independence test for constraint-based learning of causal structure. In Proceedings of the Ninteenth Conference on Uncertainty in Artificial Intelligence, Meek, C. & Kj\u00e6rulff, U. (eds). Morgan Kaufmann, 167\u2013174."},{"key":"S0269888910000251_ref129","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-8640.1989.tb00324.x"},{"key":"S0269888910000251_ref240","doi-asserted-by":"publisher","DOI":"10.1023\/A:1007665907178"},{"key":"S0269888910000251_ref341","doi-asserted-by":"publisher","DOI":"10.1016\/0004-3702(86)90072-X"},{"key":"S0269888910000251_ref25","doi-asserted-by":"crossref","unstructured":"Beinlich I. , Suermondt H. , Chavez R. , Cooper G. 1989. The ALARM monitoring system: a case study with two probabilistic inference techniques for belief networks. In Proceedings of the Second European Conference on Artificial Intelligence in Medicine (AIME 89), Lecture Notes in Medical Informatics 38, 247\u2013256, Springer.","DOI":"10.1007\/978-3-642-93437-7_28"},{"key":"S0269888910000251_ref124","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-36131-6_19"},{"key":"S0269888910000251_ref305","doi-asserted-by":"crossref","first-page":"256","DOI":"10.1055\/s-0038-1634847","article-title":"Probabilistic diagnosis using a reformulation of the INTERNIST-1\/QMR knowledge base: II. Evaluation of diagnostic performance","volume":"30","author":"Middleton","year":"1991","journal-title":"Methods of Information in Medicine"},{"key":"S0269888910000251_ref190","volume-title":"Discovering Causal Structure: Artificial Intelligence, Philosophy of Science, and Statistical Modeling","author":"Glymour","year":"1987"},{"key":"S0269888910000251_ref48","doi-asserted-by":"crossref","unstructured":"Burge J. , Lane T. 2006. Improving Bayesian network structure search with random variable aggregation hierarchies. In Proceedings of the Seventeenth European Conference on Machine Learning (ECML 2006), Lecture Notes in Artificial Intelligence 4212, 66\u201377. Springer.","DOI":"10.1007\/11871842_11"},{"key":"S0269888910000251_ref308","doi-asserted-by":"publisher","DOI":"10.1016\/0888-613X(96)00012-6"},{"key":"S0269888910000251_ref45","doi-asserted-by":"crossref","unstructured":"Buntine W. 1991. Theory refinement on Bayesian networks. In Proceedings of the Seventh Annual Conference on Uncertainty in Artificial Intelligence (UAI \u201991), Ambrosio, B. D. & Smets, P. (eds). Morgan Kaufmann, 52\u201360.","DOI":"10.1016\/B978-1-55860-203-8.50010-3"},{"key":"S0269888910000251_ref118","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijar.2006.06.009"},{"key":"S0269888910000251_ref147","first-page":"132","volume-title":"Proceedings of the Eighteenth National Conference on Artificial Intelligence (AAAI-02)","author":"Elidan","year":"2002"},{"key":"S0269888910000251_ref16","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1214\/aos\/1031833662","article-title":"A characterization of Markov equivalence classes for acyclic digraphs","volume":"25","author":"Andersson","year":"1997","journal-title":"The Annals of Statistics"},{"key":"S0269888910000251_ref227","first-page":"175","volume-title":"Proceedings of the Seventh Pacific Symposium on Biocomputing","author":"Imoto","year":"2002"},{"key":"S0269888910000251_ref101","doi-asserted-by":"crossref","unstructured":"Dagum P. , Galper A. , Horvitz E. 1992. Dynamic network models for forecasting. In Proceedings of the Eighth Conference on Uncertainty in Artificial Intelligence (UAI-92), Dubois, D., Wellman, M. P., D\u2019Ambrosio, B. & Smets, P. (eds). Morgan Kaufmann, 41\u201348.","DOI":"10.1016\/B978-1-4832-8287-9.50010-4"},{"key":"S0269888910000251_ref314","first-page":"176","volume-title":"Proceedings of the Fifth Mexican International Conference in Computer Science (ENC \u201904)","author":"Morales","year":"2004"},{"key":"S0269888910000251_ref20","doi-asserted-by":"crossref","first-page":"453","DOI":"10.1093\/oso\/9780198526155.003.0025","volume-title":"Bayesian Statistics 7: Proceedings of the Seventh Valencia International Meeting","author":"Beal","year":"2003"},{"key":"S0269888910000251_ref89","doi-asserted-by":"publisher","DOI":"10.1007\/BF00994110"},{"key":"S0269888910000251_ref355","doi-asserted-by":"publisher","DOI":"10.1002\/9780470994559"},{"key":"S0269888910000251_ref268","doi-asserted-by":"publisher","DOI":"10.1016\/B978-1-4832-1451-1.50034-2"},{"key":"S0269888910000251_ref40","unstructured":"Boyen X. , Friedman N. , Koller D. 1999. Discovering the hidden structure of complex dynamic systems. In Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence (UAI-99), Prade, H. & Laskey, K. (eds). Morgan Kaufmann, 91\u2013100."},{"key":"S0269888910000251_ref399","unstructured":"Spirtes P. , Glymour C. 1990a. An Algorithm for Fast Recovery of Sparse Causal Graphs. Report CMU-PHIL-15, Department of Philosophy, Carnegie Mellon University."},{"key":"S0269888910000251_ref17","first-page":"255","volume-title":"Computer-aided Electromyography and Expert Systems","author":"Andreassen","year":"1989"},{"key":"S0269888910000251_ref361","volume-title":"Proceedings of the Seventh International Workshop on Artificial Intelligence and Statistics","author":"Ramoni","year":"1999"},{"key":"S0269888910000251_ref19","unstructured":"Bauer E. , Koller D. , Singer Y. 1997. Update rules for parameter estimation in Bayesian networks. In Proceedings of the Thirteenth Annual Conference on Uncertainty in Artificial Intelligence (UAI-97), Geiger, D. & Shenoy, P. P. (eds). Morgan Kaufmann, 3\u201313."},{"key":"S0269888910000251_ref7","unstructured":"Acid S. , de Campos L. M. 1996a. An algorithm for finding minimum d-separating sets in belief networks. In Proceedings of the Twelfth Conference on Uncertainty in Artificial Intelligence (UAI-96), Horvitz, E. & Jensen, F. (eds). Morgan Kaufmann, 3\u201310."},{"key":"S0269888910000251_ref349","first-page":"606","volume-title":"Proceedings of the Thirteenth International Joint Conference on Artificial Intelligence (IJCAI 93)","author":"Poole","year":"1993a"},{"key":"S0269888910000251_ref36","doi-asserted-by":"crossref","unstructured":"Bouckaert R. R. 1994c. A Stratified Simulation Scheme for Inference in Bayesian Belief Networks. Technical report UU-CS-1994-16, Department of Computer Science, Utrecht University.","DOI":"10.1016\/B978-1-55860-332-5.50019-5"},{"key":"S0269888910000251_ref162","first-page":"274","volume-title":"Proceedings of the Twelfth Conference on Uncertainty in Artificial Intelligence (UAI-96)","author":"Friedman","year":"1996"},{"key":"S0269888910000251_ref73","first-page":"507","article-title":"Optimal structure identification with greedy search","volume":"3","author":"Chickering","year":"2002","journal-title":"Journal of Machine Learning Research"},{"key":"S0269888910000251_ref310","unstructured":"Monti S. , Cooper G. F. 1997b. Learning Hybrid Bayesian Networks from Data. Technical report ISSP-97-01, Intelligent Systems Program, University of Pittsburgh."},{"key":"S0269888910000251_ref60","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4612-2270-5"},{"key":"S0269888910000251_ref348","first-page":"2251","article-title":"Finding optimal Bayesian network given a super-structure","volume":"9","author":"Perrier","year":"2008","journal-title":"Journal of Machine Learning Research"},{"key":"S0269888910000251_ref159","first-page":"165","volume-title":"Proceedings of the Thirteenth Conference on Uncertainty in Artificial Intelligence (UAI-97)","author":"Friedman","year":"1997"},{"key":"S0269888910000251_ref199","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-24674-9_31"},{"key":"S0269888910000251_ref185","doi-asserted-by":"publisher","DOI":"10.1023\/A:1020202028934"},{"key":"S0269888910000251_ref279","doi-asserted-by":"crossref","first-page":"568","DOI":"10.1007\/11903697_72","volume-title":"Proceedings of the Sixth International Conference on Simulated Evolution and Learning (SEAL 2006)","author":"Li","year":"2006"},{"key":"S0269888910000251_ref187","unstructured":"Giudici P. , Green P. , Tarantola C. 1999. Efficient model determination for discrete graphical models. Discussion paper 99-93, Department of Statistics, Athens University of Economics and Business."},{"key":"S0269888910000251_ref33","doi-asserted-by":"crossref","unstructured":"Bouckaert R. R. 1993. Probabilistic network construction using the minimum description length principle. In Symbolic and Quantitative Approaches to Reasoning and Uncertainty: European Conference ECSQARU \u201993, Lecture Notes in Computer Science 747, 41\u201348, Springer.","DOI":"10.1007\/BFb0028180"},{"key":"S0269888910000251_ref28","doi-asserted-by":"publisher","DOI":"10.1002\/int.10084"},{"key":"S0269888910000251_ref144","first-page":"144","volume-title":"Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence (UAI-01)","author":"Elidan","year":"2001"},{"key":"S0269888910000251_ref102","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1613\/jair.2681","article-title":"Learning Bayesian network equivalence classes with ant colony optimization","volume":"35","author":"Daly","year":"2009","journal-title":"Journal of Artificial Intelligence Research"},{"key":"S0269888910000251_ref210","doi-asserted-by":"crossref","first-page":"540","DOI":"10.3923\/itj.2006.540.545","article-title":"Research on learning Bayesian networks by particle swarm optimization","volume":"5","author":"Heng","year":"2006","journal-title":"Information Technology Journal"},{"key":"S0269888910000251_ref245","doi-asserted-by":"publisher","DOI":"10.1109\/ICNN.1995.488968"},{"key":"S0269888910000251_ref313","first-page":"552","volume-title":"Proceedings of the Twentieth International Conference on Machine Learning","author":"Moore","year":"2003"},{"key":"S0269888910000251_ref122","first-page":"109","volume-title":"Proceedings of the Third International Symposium on Adaptive Systems: Evolutionary Computation and Probabilistic Graphical Models","author":"de Campos","year":"2001"},{"key":"S0269888910000251_ref174","first-page":"156","volume-title":"Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence (UAI-98)","author":"Geiger","year":"1998"},{"key":"S0269888910000251_ref32","unstructured":"B\u00f8ttcher S. G. 2004. Learning Bayesian Networks with Mixed Variables. PhD thesis, Department of Mathematical Sciences, Aalborg University."},{"key":"S0269888910000251_ref13","unstructured":"Aliferis C. F. , Tsamardinos I. 2002. Algorithms for Large-scale Local Causal Discovery and Feature Selection in the Presence of Limited Sample or Large Causal Neighbourhoods. Technical report DSL-02-08, Department of Biomedical Informatics, Vanderbilt University."},{"key":"S0269888910000251_ref183","first-page":"171","volume-title":"Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence (UAI-01)","author":"Gillispie","year":"2001"},{"key":"S0269888910000251_ref80","doi-asserted-by":"publisher","DOI":"10.1016\/j.artint.2006.03.001"},{"key":"S0269888910000251_ref242","first-page":"613","article-title":"Estimating high-dimensional directed acyclic graphs with the PC-algorithm","volume":"8","author":"Kalisch","year":"2007","journal-title":"Journal of Machine Learning Research"},{"key":"S0269888910000251_ref46","doi-asserted-by":"publisher","DOI":"10.1109\/69.494161"},{"key":"S0269888910000251_ref9","unstructured":"Acid S. , de Campos L. M. 1996c. BENEDICT: an algorithm for learning probabilistic Bayesian networks. In Proceedings of the Sixth International Conference on Information Processing and Management of Uncertainty in Knowledge-based Systems, Granada, Spain, 979\u2013984."},{"key":"S0269888910000251_ref39","unstructured":"Boyen X. , Koller D. 1998. Tractable inference for complex stochastic processes. In Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence (UAI-98), Cooper, G. F. & Moral, S. (eds). Morgan Kaufmann, 33\u201342."},{"key":"S0269888910000251_ref69","unstructured":"Cheng J. , Greiner R. 1999. Comparing Bayesian network classifiers. In Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence (UAI-99), Prade, H. & Laskey, K. (eds). Morgan Kaufmann, 101\u2013108."},{"key":"S0269888910000251_ref137","first-page":"170","volume-title":"Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence (UAI-94)","author":"Draper","year":"1994"},{"key":"S0269888910000251_ref106","unstructured":"Darwiche A. 2002. A logical approach to factoring belief networks. In Proceedings of the Eight International Conference on Principles of Knowledge Representation and Reasoning (KR-02), Fensel, D., Giunchiglia, F., McGuinness, D. L. & Williams, M.-A. (eds). Morgan Kaufmann, 409\u2013420."},{"key":"S0269888910000251_ref184","doi-asserted-by":"publisher","DOI":"10.1016\/S0004-3702(02)00264-3"},{"key":"S0269888910000251_ref233","volume-title":"Information Science and Statistics","author":"Jensen","year":"2007"},{"key":"S0269888910000251_ref61","doi-asserted-by":"publisher","DOI":"10.1023\/A:1007372016040"},{"key":"S0269888910000251_ref138","first-page":"187","volume-title":"Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence (UAI-94)","author":"Druzdzel","year":"1994"},{"key":"S0269888910000251_ref52","doi-asserted-by":"publisher","DOI":"10.5840\/monist20018429"},{"key":"S0269888910000251_ref5","doi-asserted-by":"crossref","first-page":"445","DOI":"10.1613\/jair.1061","article-title":"Searching for Bayesian network structures in the space of restricted acyclic partially directed graphs","volume":"18","author":"Acid","year":"2003","journal-title":"Journal of Artificial Intelligence Research"},{"key":"S0269888910000251_ref331","first-page":"438","volume-title":"Proceedings of the First International Conference on Artificial Intelligence (IC-AI \u201903)","author":"Novobilski","year":"2003"},{"key":"S0269888910000251_ref328","first-page":"432","volume-title":"Proceedings of the Third Pacific-Asia Conference on Methodologies for Knowledge Discovery and Data Mining (PAKDD \u201999)","author":"Neil","year":"1999"},{"key":"S0269888910000251_ref4","doi-asserted-by":"publisher","DOI":"10.1016\/S0888-613X(01)00041-X"},{"key":"S0269888910000251_ref22","doi-asserted-by":"crossref","unstructured":"Becker A. , Geiger D. 1994. Approximation algorithms for the loop cutset problem. In Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence (UAI-94), de Mantaras, R. L. & Poole, D. (eds). Morgan Kaufmann, 60\u201368.","DOI":"10.1016\/B978-1-55860-332-5.50013-4"},{"key":"S0269888910000251_ref189","doi-asserted-by":"crossref","unstructured":"Glymour C. , Scheines R. , Spirtes P. , Kelly K. 1986. Discovering Causal Structure: Artifical Intelligence, Philosophy of Science and Statistical Modeling. Report CMU-PHIL-1, Department of Philosophy, Carnegie Mellon University.","DOI":"10.1016\/B978-0-12-286961-7.50010-X"},{"key":"S0269888910000251_ref166","first-page":"196","volume-title":"Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence (UAI-99)","author":"Friedman","year":"1999b"},{"key":"S0269888910000251_ref243","first-page":"346","volume-title":"Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence (UAI-95)","author":"Kanazawa","year":"1995"},{"key":"S0269888910000251_ref219","doi-asserted-by":"publisher","DOI":"10.1016\/S0888-613X(96)00069-2"},{"key":"S0269888910000251_ref234","first-page":"269","article-title":"Bayesian updating in causal probabilistic networks by local computations","volume":"4","author":"Jensen","year":"1990","journal-title":"Computational Statistics Quarterly"},{"key":"S0269888910000251_ref255","first-page":"294","volume-title":"Proceedings of the Thirteenth Conference on Uncertainty in Artificial Intelligence (UAI-97)","author":"Kj\u00e6rulff","year":"1997"},{"key":"S0269888910000251_ref57","doi-asserted-by":"publisher","DOI":"10.1016\/S0888-613X(99)00041-9"},{"key":"S0269888910000251_ref258","unstructured":"Ko\u010dka T. , Bouckaert R.R. , Studen\u00fd M. 2001. On the Inclusion Problem. Research report 2010, Institute of Information Theory and Automation, Prague."},{"key":"S0269888910000251_ref358","doi-asserted-by":"publisher","DOI":"10.1023\/A:1010968702992"},{"key":"S0269888910000251_ref64","unstructured":"Chavira M. , Darwiche A. 2007. Compiling Bayesian networks using variable elimination. In Proceedings of the Twentieth International Joint Conference on Artificial Intelligence, Veloso, M. M. (ed.). Morgan Kaufmann, 2443\u20132449."},{"key":"S0269888910000251_ref208","doi-asserted-by":"publisher","DOI":"10.1007\/BF00994016"},{"key":"S0269888910000251_ref463","first-page":"459","article-title":"A recursive method for structural learning of directed acyclic graphs","volume":"9","author":"Xie","year":"2008","journal-title":"Journal of Machine Learning Research"},{"key":"S0269888910000251_ref337","doi-asserted-by":"publisher","DOI":"10.1016\/j.artint.2003.04.004"},{"key":"S0269888910000251_ref120","doi-asserted-by":"publisher","DOI":"10.1016\/S0888-613X(99)00042-0"},{"key":"S0269888910000251_ref365","first-page":"301","volume-title":"Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics","author":"Riggelsen","year":"2005"},{"key":"S0269888910000251_ref18","first-page":"1009","volume-title":"Advances in Neural Information Processing Systems 15 (NIPS*2002)","author":"Bach","year":"2003"},{"key":"S0269888910000251_ref65","first-page":"153","volume-title":"Advances in Knowledge Discovery and Data Mining","author":"Cheeseman","year":"1996"},{"key":"S0269888910000251_ref92","doi-asserted-by":"crossref","unstructured":"Cotta C. , Muruz\u00e1bal J. 2002. Towards a more efficient evolutionary induction of Bayesian networks. In Proceedings of the Seventh International Conference on Parallel Problem Solving from Nature (PPSN VII), Lecture Notes in Computer Science 2439, 730\u2013739. Springer.","DOI":"10.1007\/3-540-45712-7_70"},{"key":"S0269888910000251_ref85","doi-asserted-by":"publisher","DOI":"10.1109\/TIT.1968.1054142"},{"key":"S0269888910000251_ref173","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-72393-6_51"},{"key":"S0269888910000251_ref251","doi-asserted-by":"publisher","DOI":"10.1016\/B978-1-4832-8287-9.50021-9"},{"key":"S0269888910000251_ref430","first-page":"647","volume-title":"Advances in Neural Information Processing Systems 13 (NIPS*2000)","author":"Tong","year":"2001a"},{"key":"S0269888910000251_ref91","doi-asserted-by":"crossref","unstructured":"Correa E. S. , Freitas A. A. , Johnson C. G. 2007. Particle swarm and Bayesian networks applied to attribute selection for protein functional classification. In Proceedings of the Genetic and Evolutionary Computation Conference, Lipson, H. (ed.). ACM, 2651\u20132658.","DOI":"10.1145\/1274000.1274081"},{"key":"S0269888910000251_ref86","doi-asserted-by":"publisher","DOI":"10.1016\/0004-3702(90)90060-D"},{"key":"S0269888910000251_ref27","first-page":"416","volume-title":"Advances in Neural Information Processing Systems 10 (NIPS*1997)","author":"Bishop","year":"1998"},{"key":"S0269888910000251_ref15","doi-asserted-by":"crossref","unstructured":"Anderson B. , Moore A. 2005. Active learning for hidden Markov models: objective functions and algorithms. In Proceedings of the Twenty-Second International Conference on Machine Learning (ICML 2005), De Raedt, L. & Wrobel, S. (eds). ACM, 9\u201316.","DOI":"10.1145\/1102351.1102353"},{"key":"S0269888910000251_ref44","unstructured":"Brown L. E. , Tsamardinos I. , Aliferis C. F. 2005. A comparison of novel and state-of-the-art polynomial Bayesian network learning algorithms. In Proceedings of the Twentieth National Conference On Artificial Intelligence, Veloso, M. M. & Kambhampati, S. (eds). 2, AAAI Press, 739\u2013745."},{"key":"S0269888910000251_ref301","unstructured":"Meek C. 1997. Graphical Models: Selecting Causal and Statistical Models. PhD thesis, Department of Philosophy, Carnegie Mellon University, Pittsburgh, PA."},{"key":"S0269888910000251_ref130","first-page":"137","volume-title":"Proceedings of the International Conference on Computational Intelligence and Security","author":"Delaplace","year":"2006"},{"key":"S0269888910000251_ref359","unstructured":"Ramoni M. , Sebastiani P. 1997a. Learning Bayesian Networks from Incomplete Databases. Technical report KMI-TR-43, Knowledge Media Institute, The Open University."},{"key":"S0269888910000251_ref200","doi-asserted-by":"publisher","DOI":"10.1093\/bjps\/55.1.147"},{"key":"S0269888910000251_ref10","doi-asserted-by":"crossref","unstructured":"Acid S. , de Campos L. M. , Huete J. F. 2001. The search of causal orderings: a short cut for learning belief networks. In Symbolic and Quantitative Approaches to Reasoning with Uncertainty: Proceedings of the Sixth European Conference, ECSQARU 2001, Lecture Notes in Artificial Intelligence 2143, 216\u2013227. Springer.","DOI":"10.1007\/3-540-44652-4_20"},{"key":"S0269888910000251_ref453","first-page":"567","volume-title":"Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence (UAI-94)","author":"Wellman","year":"1994"},{"key":"S0269888910000251_ref12","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2105-6-S3-S4"},{"key":"S0269888910000251_ref335","first-page":"557","volume-title":"Proceedings of the Ninth Pacific Symposium on Biocomputing","author":"Ott","year":"2004"},{"key":"S0269888910000251_ref29","doi-asserted-by":"crossref","unstructured":"Borchani H. , Amor N. B. , Mellouli K. 2006. Learning Bayesian network equivalence classes from incomplete data. In Proceedings of the Ninth International Conference on Discovery Science, Lecture Notes in Artificial Intelligence 4265, 291\u2013295, Springer.","DOI":"10.1007\/11893318_29"},{"key":"S0269888910000251_ref434","unstructured":"Tsamardinos I. , Aliferis C. F. , Statnikov A. , Brown L. E. 2003c. Scaling-up Bayesian Network Learning to Thousands of Variables Using Local Learning Techniques. Technical report DSL-03-02, Department of Biomedical Informatics, Vanderbilt University, Nashville, Tennessee."},{"key":"S0269888910000251_ref449","first-page":"45","volume-title":"Proceedings of the International Conference on Computational Intelligence and Security","author":"Wang","year":"2006"},{"key":"S0269888910000251_ref70","unstructured":"Cheng J. , Bell D. A. , Liu W. 1997. An algorithm for Bayesian belief network construction from data. In Proceedings of the Sixth International Workshop on Artificial Intelligence and Statistics, Smyth, P. & Madigan, D. (eds). Fort Lauderdale, USA, 83\u201390."},{"key":"S0269888910000251_ref34","unstructured":"Bouckaert R. R. 1994a. Probabilistic Network Construction Using the Minimum Description Length Principle. Technical report RUU-CS-94-27, Department of Computer Science, Utrecht University."},{"key":"S0269888910000251_ref87","doi-asserted-by":"publisher","DOI":"10.1007\/BF00962823"},{"key":"S0269888910000251_ref43","unstructured":"Brown L. E. , Tsamardinos I. , Aliferis C. F. 2004. A novel algorithm for scalable and accurate Bayesian network learning. In Proceedings of the Eleventh World Congress on Medical Informatics (MEDINFO) Fieschi, M., Coiera, E. & Li, Y. J. (eds). 1, IOS Press, 711\u2013715."},{"key":"S0269888910000251_ref116","first-page":"2149","article-title":"A scoring function for learning bayesian networks based on mutual information and conditional independence tests","volume":"7","author":"de Campos","year":"2006","journal-title":"Journal of Machine Learning Research"},{"key":"S0269888910000251_ref212","doi-asserted-by":"publisher","DOI":"10.1016\/S0888-613X(97)10004-4"},{"key":"S0269888910000251_ref405","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4612-2748-9"},{"key":"S0269888910000251_ref126","doi-asserted-by":"publisher","DOI":"10.1002\/int.10085"},{"key":"S0269888910000251_ref431","first-page":"863","volume-title":"Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence (IJCAI 01)","author":"Tong","year":"2001b"},{"key":"S0269888910000251_ref134","doi-asserted-by":"publisher","DOI":"10.1080\/01969729408902314"},{"key":"S0269888910000251_ref38","unstructured":"Boutilier C. , Friedman N. , Goldszmidt M. , Koller D. 1996. Context-specific independence in Bayesian networks. In Proceedings of the Twelfth Conference on Uncertainty in Artificial Intelligence (UAI-96), Horvitz, E. & Jensen, F. (eds). Morgan Kaufmann, 115\u2013123."},{"key":"S0269888910000251_ref371","doi-asserted-by":"publisher","DOI":"10.1109\/3468.686701"},{"key":"S0269888910000251_ref131","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1111\/j.2517-6161.1977.tb01600.x","article-title":"Maximum likelihood from incomplete data via the EM algorithm","volume":"39","author":"Dempster","year":"1977","journal-title":"Journal of the Royal Statistical Socitety. Series B (Methodological)"},{"key":"S0269888910000251_ref286","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1007\/978-3-540-74690-4_6","volume-title":"Proceedings of the Seventeenth International Conference on Artificial Neural Networks (ICANN 2007)","author":"Liu","year":"2007b"},{"key":"S0269888910000251_ref179","first-page":"283","volume-title":"Proceedings of the Twelfth Conference on Uncertainty in Artificial Intelligence (UAI-96)","author":"Geiger","year":"1996"},{"key":"S0269888910000251_ref21","doi-asserted-by":"publisher","DOI":"10.1016\/S0004-3702(00)00075-8"},{"key":"S0269888910000251_ref110","unstructured":"Darwiche A. 1998. Dynamic jointrees. In Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence (UAI-98), Cooper G. F. & Moral S. (eds). Morgan Kaufmann, 97\u2013104."},{"key":"S0269888910000251_ref112","unstructured":"Dasgupta S. 1999. Learning polytrees. In Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence (UAI-99), Prade, H. & Laskey, K. (eds). Morgan Kaufmann, 134\u2013141."},{"key":"S0269888910000251_ref169","unstructured":"Fu L. D. 2005. A Comparison of State-of-the-Art Algorithms for Learning Bayesian Network Structure from Continuous Data. Master\u2019s thesis, Vanderbilt University."},{"key":"S0269888910000251_ref63","doi-asserted-by":"publisher","DOI":"10.1016\/B978-0-444-88738-2.50022-1"},{"key":"S0269888910000251_ref98","doi-asserted-by":"publisher","DOI":"10.1002\/net.3230230506"},{"key":"S0269888910000251_ref346","doi-asserted-by":"publisher","DOI":"10.1016\/0004-3702(91)90030-N"},{"key":"S0269888910000251_ref152","first-page":"1878","volume-title":"Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence (IJCAI 95)","author":"Forbes","year":"1995"},{"key":"S0269888910000251_ref228","unstructured":"Jaakkola T. S. , Jordan M. I. 1996. Computing upper and lower bounds on likelihoods in intractable networks. A.I. Memo 1571, Artficial Intelligence Lab, Massachusetts Institute of Technology."},{"key":"S0269888910000251_ref105","doi-asserted-by":"publisher","DOI":"10.1016\/S0004-3702(00)00069-2"},{"key":"S0269888910000251_ref107","doi-asserted-by":"publisher","DOI":"10.1145\/765568.765570"},{"key":"S0269888910000251_ref55","first-page":"527","article-title":"On inclusion-driven learning of Bayesian networks","volume":"4","author":"Castelo","year":"2003","journal-title":"Journal of Machine Learning Research"},{"key":"S0269888910000251_ref154","first-page":"125","volume-title":"Proceedings of the Fourteenth International Conference on Machine Learning (ICML \u201997)","author":"Friedman","year":"1997"},{"key":"S0269888910000251_ref193","doi-asserted-by":"publisher","DOI":"10.1109\/SNPD.2007.472"},{"key":"S0269888910000251_ref315","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2001.989547"},{"key":"S0269888910000251_ref321","first-page":"458","volume-title":"Proceedings of the Genetic and Evolutionary Computation Conference","author":"Myers","year":"1999a"},{"key":"S0269888910000251_ref462","first-page":"564","volume-title":"Proceedings of the Twelfth Conference on Uncertainty in Artificial Intelligence (UAI-96)","author":"Xiang","year":"1996"},{"key":"S0269888910000251_ref197","unstructured":"Guo Y.-Y. , Wong M.-L. , Cai Z.-H. 2006. A novel hybrid evolutionary algorithm for learning Bayesian networks from incomplete data. In Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2006), 916\u2013923."},{"key":"S0269888910000251_ref206","doi-asserted-by":"publisher","DOI":"10.1109\/3468.541341"},{"key":"S0269888910000251_ref205","unstructured":"Heckerman D. 1995b. A Tutorial on Learning with Bayesian Networks. Technical report MSR-TR-95-06, Microsoft Research."},{"key":"S0269888910000251_ref294","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1080\/10618600.1997.10474735","article-title":"Graphical explanation in belief networks","volume":"6","author":"Madigan","year":"1997","journal-title":"Journal of Computational and Graphical Statistics"},{"key":"S0269888910000251_ref216","first-page":"500","volume-title":"Advances in Neural Information Processing Systems 8 (NIPS*1995)","author":"Hofmann","year":"1996"},{"key":"S0269888910000251_ref186","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/86.4.785"},{"key":"S0269888910000251_ref303","doi-asserted-by":"publisher","DOI":"10.1007\/11681960_8"},{"key":"S0269888910000251_ref257","first-page":"269","volume-title":"Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence (UAI-01)","author":"Ko\u010dka","year":"2001"},{"key":"S0269888910000251_ref244","first-page":"251","volume-title":"Proceedings of the Eighteenth Conference on Uncertainty in Artificial Intelligence (UAI-02)","author":"Kayaalp","year":"2002"},{"key":"S0269888910000251_ref204","unstructured":"Heckerman D. 1995a. A Bayesian Approach to Learning Causal Networks. Technical report MSR-TR-95-04, Microsoft Research."},{"key":"S0269888910000251_ref396","doi-asserted-by":"publisher","DOI":"10.1016\/B978-0-444-70058-2.50009-7"},{"key":"S0269888910000251_ref252","doi-asserted-by":"publisher","DOI":"10.1007\/BF01890544"},{"key":"S0269888910000251_ref195","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-008-5057-7"},{"key":"S0269888910000251_ref196","first-page":"1","volume-title":"Papers from the AAAI Workshop on Real-Time Decision Support and Diagnosis Systems","author":"Guo","year":"2002"},{"key":"S0269888910000251_ref450","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiolchem.2007.08.005"},{"key":"S0269888910000251_ref311","first-page":"404","volume-title":"Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence (UAI-98)","author":"Monti","year":"1998"},{"key":"S0269888910000251_ref229","first-page":"487","volume-title":"Advances in Neural Information Processing Systems 9 (NIPS*1996)","author":"Jaakkola","year":"1997"},{"key":"S0269888910000251_ref226","doi-asserted-by":"crossref","first-page":"670","DOI":"10.1007\/11893028_75","volume-title":"Proceedings of the Thirteenth International Conference on Neural Information Processing (ICONIP 2006)","author":"Hwang","year":"2006"},{"key":"S0269888910000251_ref333","first-page":"1162","volume-title":"Proceedings of the Ninteenth Australian Joint Conference on Artificial Intelligence (AI 2006)","author":"O\u2019Donnell","year":"2006b"},{"key":"S0269888910000251_ref220","first-page":"86","volume-title":"Proceedings of the Eight International Conference on Computer Supported Cooperative Work in Design (CSCWD 2004)","author":"Huang","year":"2005"},{"key":"S0269888910000251_ref218","first-page":"383","volume-title":"Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2002)","author":"Hsu","year":"2002"},{"key":"S0269888910000251_ref273","doi-asserted-by":"publisher","DOI":"10.1016\/0167-9473(93)E0056-A"},{"key":"S0269888910000251_ref248","first-page":"190","volume-title":"Proceedings of the Eighth International Joint Conference on Artificial Intelligence (IJCAI 83)","author":"Kim","year":"1983"},{"key":"S0269888910000251_ref230","first-page":"163","volume-title":"Learning in Graphical Models","author":"Jaakkola","year":"1999a"},{"key":"S0269888910000251_ref325","volume-title":"Learning in Graphical Models","author":"Neal","year":"1999"},{"key":"S0269888910000251_ref104","doi-asserted-by":"publisher","DOI":"10.1145\/502090.502091"},{"key":"S0269888910000251_ref254","first-page":"374","volume-title":"Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence (UAI-94)","author":"Kj\u00e6rulff","year":"1994"},{"key":"S0269888910000251_ref207","unstructured":"Heckerman D. , Geiger D. 1995 . Likelihoods and Parameter Priors for Bayesian Networks. Technical report MSR-TR-95-54, Microsoft Research."},{"key":"S0269888910000251_ref406","first-page":"499","volume-title":"Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence (UAI-95)","author":"Spirtes","year":"1995"},{"key":"S0269888910000251_ref211","doi-asserted-by":"publisher","DOI":"10.1016\/B978-0-444-70396-5.50019-4"},{"key":"S0269888910000251_ref224","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btg313"},{"key":"S0269888910000251_ref238","first-page":"338","volume-title":"Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence (UAI-95)","author":"John","year":"1995"},{"key":"S0269888910000251_ref253","unstructured":"Kj\u00e6rulff U. 1993. Approximation of Bayesian Networks Through Edge Removals. Technical report IR-93-2007, Department of Mathematics and Computer Science, Aalborg University."},{"key":"S0269888910000251_ref302","first-page":"366","volume-title":"Proceedings of the Thirteenth Conference on Uncertainty in Artificial Intelligence (UAI-97)","author":"Meek","year":"1997"},{"key":"S0269888910000251_ref398","first-page":"392","volume-title":"Proceedings of the Seventh Annual Conference on Uncertainty in Artificial Intelligence (UAI-91)","author":"Spirtes","year":"1991"},{"key":"S0269888910000251_ref439","doi-asserted-by":"publisher","DOI":"10.1080\/01969720802039594"},{"key":"S0269888910000251_ref198","unstructured":"Guyon I. , Aliferis C. , Cooper G. , Elisseeff A. , Pellet J.-P. , Spirtes P. , Statnikov A. 2008. Design and analysis of the causation and prediction challenge. In Causation and Prediction Challenge (WCCI 2008), Lawrence, N. (ed.). 3, JMLR Workshop and Conference Proceedings, Journal of Machine Learning Research, 1\u201333."},{"key":"S0269888910000251_ref261","volume-title":"Bayesian Artificial Intelligence","author":"Korb","year":"2004"},{"key":"S0269888910000251_ref176","first-page":"196","volume-title":"Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence (UAI-95)","author":"Geiger","year":"1995"},{"key":"S0269888910000251_ref217","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-73499-4_45"},{"key":"S0269888910000251_ref231","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1613\/jair.583","article-title":"Variational probabilistic inference and the QMR-DT network","volume":"10","author":"Jaakkola","year":"1999","journal-title":"Journal of Artificial Intelligence Research"},{"key":"S0269888910000251_ref236","first-page":"611","volume-title":"Proceedings of the Second International Conference on Knowledge Science, Engineering and Management (KSEM 2007)","author":"Jia","year":"2007"},{"key":"S0269888910000251_ref281","doi-asserted-by":"publisher","DOI":"10.1016\/j.csda.2008.10.007"},{"key":"S0269888910000251_ref24","unstructured":"Becker A. , Geiger D. 1996b. A sufficiently fast algorithm for finding close to optimal junction trees. In Proceedings of the Twelfth Conference on Uncertainty in Artificial Intelligence (UAI-96), Horvitz, E. & Jensen, F. (eds). Morgan Kaufmann, 81\u201389."},{"key":"S0269888910000251_ref256","volume-title":"Information Science and Statistics","author":"Kjaerulff","year":"2008"},{"key":"S0269888910000251_ref427","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2001.989594"},{"key":"S0269888910000251_ref175","doi-asserted-by":"crossref","unstructured":"Geiger D. , Heckerman D. 1994. Learning Gaussian Networks. Technical report MSR-TR-94-10, Microsoft Research.","DOI":"10.1016\/B978-1-55860-332-5.50035-3"},{"key":"S0269888910000251_ref360","doi-asserted-by":"publisher","DOI":"10.1007\/BFb0052869"},{"key":"S0269888910000251_ref214","doi-asserted-by":"publisher","DOI":"10.1117\/12.719290"},{"key":"S0269888910000251_ref213","first-page":"54","volume-title":"Uncertainty in Artificial Intelligence 6","author":"Herskovits","year":"1991"},{"key":"S0269888910000251_ref350","doi-asserted-by":"publisher","DOI":"10.1016\/B978-1-4832-1451-1.50048-2"},{"key":"S0269888910000251_ref400","unstructured":"Spirtes P. , Glymour C. 1990b. Casual Structure among Measured Variables Preserved with Unmeasured Variables. Report CMU-PHIL-14, Department of Philosophy, Carnegie Mellon University."},{"key":"S0269888910000251_ref391","unstructured":"Silander T. , Kontkanen P. , Myllymaki P. 2007. On sensitivity of the MAP Bayesian network structure to the equivalent sample size parameter. In Proceedings of the Twenty-third Conference on Uncertainty in Artificial Intelligence (UAI-07), AUAI Press, 360\u2013367."},{"key":"S0269888910000251_ref117","doi-asserted-by":"publisher","DOI":"10.1080\/095281398146743"},{"key":"S0269888910000251_ref225","first-page":"375","volume-title":"Trends in Artificial Intelligence: Proceedings of the Seventh Pacific Rim International Conference on Artificial Intelligence (PRICAI 2002)","author":"Hwang","year":"2002"},{"key":"S0269888910000251_ref247","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1007\/3-540-45357-1_18","volume-title":"Proceedings of the Fifth Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining (PAKDD 2001)","author":"Kennett","year":"2001"},{"key":"S0269888910000251_ref249","doi-asserted-by":"publisher","DOI":"10.1109\/CEC.2006.1688488"},{"key":"S0269888910000251_ref432","first-page":"376","volume-title":"Proceedings of the Sixteenth International FLAIRS Conference","author":"Tsamardinos","year":"2003a"},{"key":"S0269888910000251_ref237","first-page":"393","volume-title":"Proceedings of the Twelfth Australian Joint Conference on Artificial Intelligence (AI\u201999)","author":"Jitnah","year":"1999"},{"key":"S0269888910000251_ref336","unstructured":"Pakzad P. , Anantharam V. 2002. Belief propagation and statistical physics. In Proceedings of the 2002 Conference on Information Sciences and Systems, Princeton University, USA."},{"key":"S0269888910000251_ref250","doi-asserted-by":"publisher","DOI":"10.1126\/science.220.4598.671"},{"key":"S0269888910000251_ref41","first-page":"129","volume-title":"Uncertainty in Artificial Intelligence 6","author":"Breese","year":"1991"},{"key":"S0269888910000251_ref264","doi-asserted-by":"publisher","DOI":"10.1214\/aoms\/1177729694"},{"key":"S0269888910000251_ref280","doi-asserted-by":"publisher","DOI":"10.1016\/0888-613X(94)90019-1"},{"key":"S0269888910000251_ref239","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-73580-9_22"},{"key":"S0269888910000251_ref373","doi-asserted-by":"publisher","DOI":"10.1016\/S0888-613X(97)00012-1"},{"key":"S0269888910000251_ref269","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-8640.1994.tb00166.x"},{"key":"S0269888910000251_ref299","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-74819-9_51"},{"key":"S0269888910000251_ref309","first-page":"578","volume-title":"Advances in Neural Information Processing Systems 9 (NIPS*1996)","author":"Monti","year":"1997a"},{"key":"S0269888910000251_ref397","doi-asserted-by":"publisher","DOI":"10.1002\/net.3230200507"},{"key":"S0269888910000251_ref260","first-page":"549","article-title":"Exact Bayesian structure discovery in Bayesian networks","volume":"5","author":"Koivisto","year":"2004","journal-title":"Journal of Machine Learning Research"},{"key":"S0269888910000251_ref441","first-page":"886","volume-title":"Proceedings of the Genetic and Evolutionary Computation Conference","author":"van Dijk","year":"2003"},{"key":"S0269888910000251_ref115","unstructured":"Dash D. , Druzdzel M. J. 1999. A hybrid anytime algorithm for the construction of causal models from sparse data. In Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence (UAI-99), Prade, H., Laskey, K. (eds). Morgan Kaufmann, 142\u2013149."},{"key":"S0269888910000251_ref90","unstructured":"Cooper G. F. , Yoo C. 1999. Causal discovery from a mixture of experimental and observational data. In Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence (UAI-99), Prade, H. & Laskey, K. (eds). Morgan Kaufmann, 116\u2013125."},{"key":"S0269888910000251_ref307","first-page":"652","volume-title":"Proceedings of the Fifth Mexican International Conference on Artificial Intelligence (MICAI 2006)","author":"Mondrag\u00f3n-Becerra","year":"2006"},{"key":"S0269888910000251_ref272","doi-asserted-by":"publisher","DOI":"10.1109\/34.537345"},{"key":"S0269888910000251_ref267","doi-asserted-by":"publisher","DOI":"10.1109\/34.667882"},{"key":"S0269888910000251_ref395","doi-asserted-by":"publisher","DOI":"10.1016\/S0167-8655(97)01050-7"},{"key":"S0269888910000251_ref415","doi-asserted-by":"publisher","DOI":"10.1093\/bjps\/axi154"},{"key":"S0269888910000251_ref276","unstructured":"Leray P. , Fran\u00e7ois O. 2005. Bayesian network structural learning and incomplete data. In Proceedings of the International and Interdisciplinary Conference on Adaptive Knowledge Representation and Reasoning (AKRR 2005), Honkela, T., K\u00f6n\u00f6ner, V., P\u00f6ll\u00e4, M. & Simula, O. (eds). Espoo, Finland, 33\u201340."},{"key":"S0269888910000251_ref270","first-page":"383","volume-title":"Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence (UAI-94)","author":"Lam","year":"1994b"},{"key":"S0269888910000251_ref300","first-page":"403","volume-title":"Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence (UAI-95)","author":"Meek","year":"1995"},{"key":"S0269888910000251_ref291","unstructured":"Madigan D. , Gavrin J. , Raftery A. E. 1994. Enhancing the Predictive Performance of Bayesian Graphical Models. Technical report 270, Department of Statistics, University of Washington."},{"key":"S0269888910000251_ref271","doi-asserted-by":"publisher","DOI":"10.1109\/3468.508827"},{"key":"S0269888910000251_ref404","doi-asserted-by":"publisher","DOI":"10.1007\/BF00356088"},{"key":"S0269888910000251_ref278","first-page":"475","article-title":"Controlling the false discovery rate of the association\/causality structure learned with the PC algorithm","volume":"10","author":"Li","year":"2009","journal-title":"Journal of Machine Learning Research"},{"key":"S0269888910000251_ref290","unstructured":"Madigan D. , Raftery A. E. , York J. C. , Bradshaw J. M. , Almond R. G. 1993. Strategies for graphical model selection. In Proceedings of the Fourth International Workshop on Artificial Intelligence and Statistics, Cheeseman, P. & Oldford, R. W. (eds). Fort Lauderdale, USA, 331\u2013336."},{"key":"S0269888910000251_ref293","doi-asserted-by":"publisher","DOI":"10.1080\/03610929608831853"},{"key":"S0269888910000251_ref296","first-page":"324","volume-title":"Proceedings of the Twenty-Second Annual Conference on Uncertainty in Artificial Intelligence (UAI-06)","author":"Mansinghka","year":"2006"},{"key":"S0269888910000251_ref11","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2003.11.002"},{"key":"S0269888910000251_ref304","doi-asserted-by":"publisher","DOI":"10.1007\/s11222-006-5535-3"},{"key":"S0269888910000251_ref312","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1613\/jair.453","article-title":"Cached sufficient statistics for efficient machine learning with large datasets","volume":"8","author":"Moore","year":"1998","journal-title":"Journal of Artificial Intelligence Research"},{"key":"S0269888910000251_ref222","first-page":"1149","volume-title":"Proceedings of the Twenty-First National Conference on Artificial Intelligence (AAAI-06)","author":"Huang","year":"2006"},{"key":"S0269888910000251_ref283","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-73053-8_35"},{"key":"S0269888910000251_ref316","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-45372-5_10"},{"key":"S0269888910000251_ref292","doi-asserted-by":"publisher","DOI":"10.2307\/1403615"},{"key":"S0269888910000251_ref265","doi-asserted-by":"publisher","DOI":"10.1016\/0004-3702(95)00119-0"},{"key":"S0269888910000251_ref282","unstructured":"Lin Y. , Druzdzel M. J. 1999. Stochastic sampling and search in belief updating algorithms for very large Bayesian networks. In Working Notes of the AAAI Spring Symposium on Search Techniques for Problem Solving under Uncertainty and Incomplete Information, Zhang, W. & Koenig, S. (eds). AAAI Press, 77\u201382."},{"key":"S0269888910000251_ref284","doi-asserted-by":"publisher","DOI":"10.1109\/ICNC.2007.467"},{"key":"S0269888910000251_ref444","first-page":"255","volume-title":"Uncertainty in Artificial Intelligence 6","author":"Verma","year":"1991"},{"key":"S0269888910000251_ref288","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2003.11.001"},{"key":"S0269888910000251_ref71","doi-asserted-by":"publisher","DOI":"10.1016\/S0004-3702(02)00191-1"},{"key":"S0269888910000251_ref317","unstructured":"Murphy K. P. 2001. Active Learning of Causal Bayes Net Structure. Technical report, Department of Computer Science, University of California, Berkeley."},{"key":"S0269888910000251_ref319","first-page":"467","volume-title":"Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence (UAI-99)","author":"Murphy","year":"1999"},{"key":"S0269888910000251_ref275","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1176347003"},{"key":"S0269888910000251_ref332","first-page":"192","volume-title":"Advances in Artificial Intelligence: Proceedings of the Ninteenth Australian Joint Conference on Artificial Intelligence (AI 2006)","author":"O\u2019Donnell","year":"2006a"},{"key":"S0269888910000251_ref382","doi-asserted-by":"publisher","DOI":"10.1287\/opre.36.4.589"},{"key":"S0269888910000251_ref345","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2003.1250992"},{"key":"S0269888910000251_ref326","volume-title":"Learning Bayesian Networks","author":"Neapolitan","year":"2004"},{"key":"S0269888910000251_ref369","first-page":"505","volume-title":"Swarm Intelligence: Focus on Ant and Particle Swarm Optimization","author":"Sahin","year":"2007"},{"key":"S0269888910000251_ref378","doi-asserted-by":"publisher","DOI":"10.1016\/B978-0-444-88738-2.50024-5"},{"key":"S0269888910000251_ref50","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2008.10.036"},{"key":"S0269888910000251_ref334","first-page":"124","article-title":"Finding optimal gene networks using biological constraints","volume":"14","author":"Ott","year":"2003","journal-title":"Genome Informatics"},{"key":"S0269888910000251_ref424","first-page":"453","volume-title":"Proceedings of the Thirteenth Conference on Uncertainty in Artificial Intelligence (UAI-97)","author":"Thiesson","year":"1997"},{"key":"S0269888910000251_ref403","unstructured":"Spirtes P. , Glymour C. , Scheines R. 1989. Causality from Probability. Report CMU-PHIL-12, Department of Philosophy, Carnegie Mellon University."},{"key":"S0269888910000251_ref383","doi-asserted-by":"publisher","DOI":"10.1007\/BF01531015"},{"key":"S0269888910000251_ref209","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1055\/s-0038-1634867","article-title":"Toward normative expert systems: part I. The Pathfinder project","volume":"31","author":"Heckerman","year":"1992","journal-title":"Methods of Information in Medicine"},{"key":"S0269888910000251_ref356","first-page":"446","volume-title":"Proceedings of the Twelfth Conference on Uncertainty in Artificial Intelligence (UAI-96)","author":"Pradhan","year":"1996"},{"key":"S0269888910000251_ref380","doi-asserted-by":"publisher","DOI":"10.1287\/opre.34.6.871"},{"key":"S0269888910000251_ref372","first-page":"477","volume-title":"Proceedings of the Twelfth Conference on Uncertainty in Artificial Intelligence (UAI-96)","author":"Santos","year":"1996"},{"key":"S0269888910000251_ref297","unstructured":"Margaritis D. 2004. Distribution-free Learning of Graphical Model Structure in Continuous Domains. Technical report TR-ISU-CS-04-06, Department of Computer Science, Iowa State University."},{"key":"S0269888910000251_ref342","doi-asserted-by":"publisher","DOI":"10.1016\/0004-3702(87)90012-9"},{"key":"S0269888910000251_ref177","doi-asserted-by":"crossref","first-page":"1344","DOI":"10.1214\/aos\/1069362752","article-title":"A characterization of the Dirichlet distribution through global and local parameter independence","volume":"25","author":"Geiger","year":"1997","journal-title":"The Annals of Statistics"},{"key":"S0269888910000251_ref340","doi-asserted-by":"publisher","DOI":"10.1016\/B978-0-444-70058-2.50031-0"},{"key":"S0269888910000251_ref344","first-page":"441","volume-title":"Proceedings of the Second International Conference on Principles of Knowledge Representation and Reasoning","author":"Pearl","year":"1991"},{"key":"S0269888910000251_ref192","doi-asserted-by":"publisher","DOI":"10.1145\/1015330.1015406"},{"key":"S0269888910000251_ref364","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2008.14"},{"key":"S0269888910000251_ref366","doi-asserted-by":"publisher","DOI":"10.1016\/0005-1098(78)90005-5"},{"key":"S0269888910000251_ref31","doi-asserted-by":"publisher","DOI":"10.1142\/S0218001408006193"},{"key":"S0269888910000251_ref323","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-74958-5_24"},{"key":"S0269888910000251_ref387","doi-asserted-by":"publisher","DOI":"10.1016\/0888-613X(95)00112-T"},{"key":"S0269888910000251_ref347","doi-asserted-by":"crossref","unstructured":"Perlman M. D. 2001. Graphical Model Search Via Essential Graphs. Technical report 367, Department of Statistics, University of Washington.","DOI":"10.1090\/conm\/287\/04790"},{"key":"S0269888910000251_ref381","doi-asserted-by":"publisher","DOI":"10.1016\/B978-0-444-70058-2.50032-2"},{"key":"S0269888910000251_ref412","first-page":"469","volume-title":"Proceedings of the Eighteenth Conference on Uncertainty in Artificial Intelligence (UAI-02)","author":"Steck","year":"2002"},{"key":"S0269888910000251_ref343","volume-title":"Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference","author":"Pearl","year":"1988"},{"key":"S0269888910000251_ref367","first-page":"1369","volume-title":"Advances in Neural Information Processing Systems 21 (NIPS*2008)","author":"Robinson","year":"2009"},{"key":"S0269888910000251_ref407","volume-title":"Causation, Prediction, and Search","author":"Spirtes","year":"2000"},{"key":"S0269888910000251_ref111","doi-asserted-by":"publisher","DOI":"10.1023\/A:1007417612269"},{"key":"S0269888910000251_ref370","first-page":"472","volume-title":"Proceedings of the Twentieth International Florida Artificial Intelligence Research Society Conference (FLAIRS 2007)","author":"Sanscartier","year":"2007"},{"key":"S0269888910000251_ref148","first-page":"1799","article-title":"\u201cIdeal parent\u201d structure learning for continuous variable Bayesian networks","volume":"8","author":"Elidan","year":"2007","journal-title":"Journal of Machine Learning Research"},{"key":"S0269888910000251_ref285","doi-asserted-by":"crossref","first-page":"454","DOI":"10.1007\/978-3-540-73871-8_42","volume-title":"Proceedings of the Third International Conference on Advanced Data Mining and Applications (ADMA 2007)","author":"Liu","year":"2007a"},{"key":"S0269888910000251_ref327","first-page":"486","volume-title":"Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence (UAI-99)","author":"Neil","year":"1999"},{"key":"S0269888910000251_ref384","unstructured":"Shaughnessy P. , Livingston G. 2005. Evaluating the Causal Explanatory Value of Bayesian Network Structure Learning Algorithms. Research paper 2005-013, Department of Computer Science, University of Massachusetts Lowell."},{"key":"S0269888910000251_ref353","first-page":"447","volume-title":"Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence (UAI-98)","author":"Poole","year":"1998"},{"key":"S0269888910000251_ref374","doi-asserted-by":"publisher","DOI":"10.1109\/69.485642"},{"key":"S0269888910000251_ref375","unstructured":"Scheines R. , Spries P. , Glymour C. 1991. Building Latent Variable Models. Technical report CMU-PHIL-19, Department of Philosophy, Carnegie Mellon University."},{"key":"S0269888910000251_ref352","first-page":"1284","volume-title":"Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence (IJCAI 97)","author":"Poole","year":"1997"},{"key":"S0269888910000251_ref339","first-page":"133","volume-title":"Proceedings of the Second National Conference on Artificial Intelligence","author":"Pearl","year":"1982"},{"key":"S0269888910000251_ref390","first-page":"445","volume-title":"Proceedings of the Twenty-second Annual Conference on Uncertainty in Artificial Intelligence (UAI-06)","author":"Silander","year":"2006"},{"key":"S0269888910000251_ref136","unstructured":"Dor D. , Tarsi M. 1992. A Simple Algorithm to Construct a Consistent Extension of a Partially Oriented Graph. Technical report R-185, Cognitive Systems Laboratory, Department of Computer Science, UCLA."},{"key":"S0269888910000251_ref392","unstructured":"Singh A. P. , Moore A. W. 2005. Finding Optimal Bayesian Networks by Dynamic Programming. Technical report CMU-CALD-05-106, School of Computer Science, Carnegie Mellon University."},{"key":"S0269888910000251_ref455","volume-title":"Bayesian Nets and Causality","author":"Williamson","year":"2005"},{"key":"S0269888910000251_ref429","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-72393-6_50"},{"key":"S0269888910000251_ref458","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2004.830334"},{"key":"S0269888910000251_ref465","doi-asserted-by":"publisher","DOI":"10.1109\/ALIFE.2007.367782"},{"key":"S0269888910000251_ref77","doi-asserted-by":"publisher","DOI":"10.1023\/A:1007469629108"},{"key":"S0269888910000251_ref394","doi-asserted-by":"publisher","DOI":"10.1016\/0888-613X(94)00016-V"},{"key":"S0269888910000251_ref418","doi-asserted-by":"publisher","DOI":"10.1016\/0888-613X(90)90003-K"},{"key":"S0269888910000251_ref448","first-page":"516","volume-title":"Proceedings of the Thirteenth International Conference on Machine Learning (ICML \u201996)","author":"Wallace","year":"1996"},{"key":"S0269888910000251_ref109","unstructured":"Darwiche A. 1995. Conditioning methods for exact and approximate inference in causal networks. In Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence (UAI-95), Besnard, P. & Hanks, S. (eds). Morgan Kaufmann, 99\u2013107."},{"key":"S0269888910000251_ref467","first-page":"154","volume-title":"Proceedings of the Joint IAPR International Workshops on Structural, Syntactic, and Statistical Pattern Recognition (SSPR 2006 and SPR 2006)","author":"Yehezkel","year":"2006"},{"key":"S0269888910000251_ref389","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1055\/s-0038-1634846","article-title":"Probabilistic diagnosis using a reformulation of the INTERNIST-1\/QMR knowledge base: I. The probabilistic model and inference algorithms","volume":"30","author":"Shwe","year":"1991","journal-title":"Methods of Information in Medicine"},{"key":"S0269888910000251_ref426","unstructured":"Thiesson B. , Meek C. , Chickering D. M. , Heckerman D. 1998b. Learning Mixtures of DAG Models. Technical report MSR-TR-97-30, Microsoft Research."},{"key":"S0269888910000251_ref461","doi-asserted-by":"publisher","DOI":"10.1023\/A:1009888910252"},{"key":"S0269888910000251_ref62","doi-asserted-by":"publisher","DOI":"10.1109\/21.384253"},{"key":"S0269888910000251_ref442","first-page":"132","volume-title":"Proceedings of the Seventh European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD 2003)","author":"van Dijk","year":"2003b"},{"key":"S0269888910000251_ref468","doi-asserted-by":"crossref","first-page":"1055","DOI":"10.1007\/978-3-540-71701-0_119","volume-title":"Proceedings of the Eleventh Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining (PAKDD 2007)","author":"Yu","year":"2007"}],"container-title":["The Knowledge Engineering Review"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.cambridge.org\/core\/services\/aop-cambridge-core\/content\/view\/S0269888910000251","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T14:44:04Z","timestamp":1767624244000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.cambridge.org\/core\/product\/identifier\/S0269888910000251\/type\/journal_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2011,5,12]]},"references-count":477,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2011,5,12]]}},"alternative-id":["S0269888910000251"],"URL":"https:\/\/doi.org\/10.1017\/s0269888910000251","relation":{},"ISSN":["0269-8889","1469-8005"],"issn-type":[{"value":"0269-8889","type":"print"},{"value":"1469-8005","type":"electronic"}],"subject":[],"published":{"date-parts":[[2011,5,12]]}}}