{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T16:11:11Z","timestamp":1770999071530,"version":"3.50.1"},"reference-count":21,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2016,12,12]],"date-time":"2016-12-12T00:00:00Z","timestamp":1481500800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["New Gener. Comput."],"published-print":{"date-parts":[[2017,1]]},"DOI":"10.1007\/s00354-016-0007-6","type":"journal-article","created":{"date-parts":[[2016,12,12]],"date-time":"2016-12-12T01:55:36Z","timestamp":1481507736000},"page":"105-124","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["An Efficient Bayesian Network Structure Learning Strategy"],"prefix":"10.1007","volume":"35","author":[{"given":"Joe","family":"Suzuki","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2016,12,12]]},"reference":[{"key":"7_CR1","doi-asserted-by":"crossref","unstructured":"Beinlich, I.A., Suermondt, H.J., Chavez, R.M., Cooper, G.F.: The ALARM monitoring system: a case study with two probabilistic inference techniques for belief networks. In: The 2nd European Conference on Artificial Intelligence in Medicine, pp. 247\u2013256. Springer, London (1989)","DOI":"10.1007\/978-3-642-93437-7_28"},{"key":"7_CR2","doi-asserted-by":"crossref","unstructured":"Binder, J., Koller, D., Russell, S., Kanazawa, K.: Adaptive probabilistic networks with hidden variables. Mach. Learn. 29(2\u20133):213\u2013244 (1997)","DOI":"10.1023\/A:1007421730016"},{"key":"7_CR3","doi-asserted-by":"crossref","unstructured":"Buntine, W.: Theory refinement on Bayesian networks. In: Uncertainty in Artificial Intelligence, pp. 52\u201360. Morgan Kaufmann, Los Angels (1991)","DOI":"10.1016\/B978-1-55860-203-8.50010-3"},{"key":"7_CR4","unstructured":"Campos, C.P., Ji, Q.: Efficient structure learning of Bayesian networks using constraints. J. Mach. Learn. Res. 12(3), 663\u2013689 (2011)"},{"key":"7_CR5","unstructured":"Chickering, D.M., Meek, C., Heckerman, D.: Large-sample learning of Bayesian networks is NP-hard. In: Uncertainty in Artificial Intelligence, pp. 124\u2013133. Morgan Kaufmann, Acapulco (2003)"},{"issue":"4","key":"7_CR6","first-page":"309","volume":"9","author":"GF Cooper","year":"1992","unstructured":"Cooper, G.F., Herskovits, E.: A Bayesian method for the induction of probabilistic networks from data. Mach. Learn. 9(4), 309\u2013347 (1992)","journal-title":"Mach. Learn."},{"key":"7_CR7","unstructured":"Cussens, J., Bartlett, M.: GOBNILP 1.6.2 User\/Developer Manual1. University of York, York (2015)"},{"key":"7_CR8","unstructured":"Fan, X., Malone, B., Yuan, C.: Finding optimal Bayesian network structures with constraints learned from data. In: Uncertainty in Artificial Intelligence, pp. 200\u2013209. AUAI Press, Corvallis (2014)"},{"key":"7_CR9","unstructured":"Jeffreys, H.: Theory of Probability. Oxford University Press, Oxford (1939)"},{"key":"7_CR10","doi-asserted-by":"crossref","unstructured":"Krichevsky, R.E., Trofimov, V.K.: The performance of universal encoding. IEEE Trans. Inf. Theory IT-27(2), 199\u2013207 (1981)","DOI":"10.1109\/TIT.1981.1056331"},{"key":"7_CR11","first-page":"557","volume":"9","author":"S Ott","year":"2004","unstructured":"Ott, S., Imoto, S., Miyano, S.: Finding optimal models for small gene networks. Pac. Symp. Biocomput. 9, 557\u2013567 (2004)","journal-title":"Pac. Symp. Biocomput."},{"key":"7_CR12","doi-asserted-by":"crossref","unstructured":"Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference (Representation and Reasoning), 2nd ed. Morgan Kaufmann, Burlington (1988)","DOI":"10.1016\/B978-0-08-051489-5.50008-4"},{"key":"7_CR13","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1016\/0005-1098(78)90005-5","volume":"14","author":"J Rissanen","year":"1978","unstructured":"Rissanen, J.: Modeling by shortest data description. Automatica 14, 465\u2013471 (1978)","journal-title":"Automatica"},{"key":"7_CR14","unstructured":"Silander, T., Myllymaki, P.: A simple approach for finding the globally optimal Bayesian network structure. In: Uncertainty in Artificial Intelligence, pp. 445\u2013452. Morgan Kaufmann, Arlington (2006)"},{"key":"7_CR15","unstructured":"Singh, A.P., Moore, A.W.: Finding optimal Bayesian networks by dynamic programming. Technical Report, Carnegie Mellon University (2005)"},{"key":"7_CR16","doi-asserted-by":"crossref","DOI":"10.1007\/978-1-4612-2748-9","volume-title":"Causation. Prediction and Search","author":"P Spirtes","year":"1993","unstructured":"Spirtes, P., Glymour, C., Scheines, R.: Causation. Prediction and Search. Springer, Berlin (1993)"},{"key":"7_CR17","doi-asserted-by":"crossref","unstructured":"Suzuki, J.: A construction of Bayesian networks from databases based on an MDL principle. In: Uncertainty in Artificial Intelligence, pp. 266\u2013273. Morgan Kaufmann, Washington DC (1993)","DOI":"10.1016\/B978-1-4832-1451-1.50037-8"},{"key":"7_CR18","unstructured":"Suzuki, J.: Learning Bayesian belief networks based on the minimum description length principle: an efficient algorithm using the b & b technique. In: International Conference on Machine Learning, pp. 462\u2013470. Morgan Kaufmann, Bari (1996)"},{"key":"7_CR19","doi-asserted-by":"crossref","unstructured":"Suzuki, J.: Efficiently learning Bayesian network structures based on the b&b strategy: a theoretical analysis. In: Advanced Methodologies for Bayesian Networks, Yokohama, Japan (2015). Published also as Lecture Notes on Artificial Intelligence 9095. Springer, Berlin (2016)","DOI":"10.1007\/978-3-319-28379-1_1"},{"key":"7_CR20","unstructured":"Tian, J.: A branch-and-bound algorithm for MDL learning Bayesian networks. In: Uncertainty in Artificial Intelligence, pp. 580\u2013588. Morgan Kaufmann, Stanford (2000)"},{"key":"7_CR21","unstructured":"Ueno, M.: Learning networks determined by the ratio of prior and data. In: Uncertainty in Artificial Intelligence, pp. 598\u2013605 (2010)"}],"container-title":["New Generation Computing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00354-016-0007-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s00354-016-0007-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s00354-016-0007-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,9,16]],"date-time":"2019-09-16T12:07:04Z","timestamp":1568635624000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s00354-016-0007-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,12,12]]},"references-count":21,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2017,1]]}},"alternative-id":["7"],"URL":"https:\/\/doi.org\/10.1007\/s00354-016-0007-6","relation":{},"ISSN":["0288-3635","1882-7055"],"issn-type":[{"value":"0288-3635","type":"print"},{"value":"1882-7055","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,12,12]]}}}