{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,5]],"date-time":"2025-10-05T12:39:38Z","timestamp":1759667978809,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":25,"publisher":"ACM","license":[{"start":{"date-parts":[[2013,8,11]],"date-time":"2013-08-11T00:00:00Z","timestamp":1376179200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100000015","name":"U.S. Department of Energy","doi-asserted-by":"publisher","award":["DE-AC05-00OR22725","BT0305000"],"award-info":[{"award-number":["DE-AC05-00OR22725","BT0305000"]}],"id":[{"id":"10.13039\/100000015","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000105","name":"Office of Cyberinfrastructure","doi-asserted-by":"publisher","award":["ARRA-NSF-OCI-0906324, NSF-OCI-1136246"],"award-info":[{"award-number":["ARRA-NSF-OCI-0906324, NSF-OCI-1136246"]}],"id":[{"id":"10.13039\/100000105","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2013,8,11]]},"DOI":"10.1145\/2501221.2501226","type":"proceedings-article","created":{"date-parts":[[2013,7,30]],"date-time":"2013-07-30T13:40:50Z","timestamp":1375191650000},"page":"31-38","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Estimating building simulation parameters via Bayesian structure learning"],"prefix":"10.1145","author":[{"given":"Richard E.","family":"Edwards","sequence":"first","affiliation":[{"name":"University of Tennessee, Knoxville TN"}]},{"given":"Joshua R.","family":"New","sequence":"additional","affiliation":[{"name":"Oak Ridge National Lab, Oak Ridge TN"}]},{"given":"Lynne E.","family":"Parker","sequence":"additional","affiliation":[{"name":"University of Tennessee, Knoxville TN"}]}],"member":"320","published-online":{"date-parts":[[2013,8,11]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Bayesian Statistics 7: Proceedings of the Seventh Valencia International Meeting, page 25","author":"Bernardo J.","year":"2003","unstructured":"J. Bernardo , M. Bayarri , J. Berger , A. Dawid , D. Heckerman , A. Smith , M. West , Hierarchical bayesian models for applications in information retrieval . In Bayesian Statistics 7: Proceedings of the Seventh Valencia International Meeting, page 25 , 2003 . J. Bernardo, M. Bayarri, J. Berger, A. Dawid, D. Heckerman, A. Smith, M. West, et al. Hierarchical bayesian models for applications in information retrieval. In Bayesian Statistics 7: Proceedings of the Seventh Valencia International Meeting, page 25, 2003."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_2_1","DOI":"10.5555\/944919.944937"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_3_1","DOI":"10.1561\/2200000016"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_4_1","DOI":"10.1162\/153244302760200696"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_5_1","DOI":"10.1016\/j.jmva.2004.02.009"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_6_1","DOI":"10.5555\/1577069.1755859"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_7_1","DOI":"10.1093\/biostatistics\/kxm045"},{"key":"e_1_3_2_1_8_1","first-page":"206","volume-title":"Science","author":"Friedman N.","year":"1999","unstructured":"N. Friedman . Learning bayesian network structure from massive datasets: The \u00d2sparse candidate\u00d3 algorithm background: Learning structure . Science , pages 206 -- 215 , 1999 . N. Friedman. Learning bayesian network structure from massive datasets: The \u00d2sparse candidate\u00d3 algorithm background: Learning structure. Science, pages 206--215, 1999."},{"key":"e_1_3_2_1_9_1","volume-title":"Large-scale reverse engineering by the lasso. Arxiv preprint q-bio\/0403012","author":"Gustafsson M.","year":"2004","unstructured":"M. Gustafsson , M. Hornquist , and A. Lombardi . Large-scale reverse engineering by the lasso. Arxiv preprint q-bio\/0403012 , 2004 . M. Gustafsson, M. Hornquist, and A. Lombardi. Large-scale reverse engineering by the lasso. Arxiv preprint q-bio\/0403012, 2004."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_10_1","DOI":"10.1023\/A:1012487302797"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_11_1","DOI":"10.1007\/s12273-008-8123-y"},{"key":"e_1_3_2_1_12_1","first-page":"169","volume-title":"Advances in Kernel Methods Support Vector Learning","author":"Joachims T.","year":"1999","unstructured":"T. Joachims . Making large-Scale SVM Learning Practical . Advances in Kernel Methods Support Vector Learning , pages 169 -- 184 , 1999 . T. Joachims. Making large-Scale SVM Learning Practical. Advances in Kernel Methods Support Vector Learning, pages 169--184, 1999."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_13_1","DOI":"10.1023\/A:1007665907178"},{"key":"e_1_3_2_1_14_1","volume-title":"Probabilistic graphical models: principles and techniques","author":"Koller D.","year":"2009","unstructured":"D. Koller and N. Friedman . Probabilistic graphical models: principles and techniques . The MIT Press , 2009 . D. Koller and N. Friedman. Probabilistic graphical models: principles and techniques. The MIT Press, 2009."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_15_1","DOI":"10.1109\/34.537345"},{"key":"e_1_3_2_1_17_1","first-page":"779","article-title":"From lasso regression to feature vector machine","volume":"18","author":"Li F.","year":"2006","unstructured":"F. Li , Y. Yang , and E. Xing . From lasso regression to feature vector machine . Advances in Neural Information Processing Systems , 18 : 779 , 2006 . F. Li, Y. Yang, and E. Xing. From lasso regression to feature vector machine. Advances in Neural Information Processing Systems, 18:779, 2006.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_18_1","volume-title":"Trans. on Pattern Analysis and Machine Intelligence","author":"Margaritis D.","year":"1999","unstructured":"D. Margaritis and S. Thrun . Bayesian network induction via local neighborhoods . Trans. on Pattern Analysis and Machine Intelligence , 1999 . D. Margaritis and S. Thrun. Bayesian network induction via local neighborhoods. Trans. on Pattern Analysis and Machine Intelligence, 1999."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_19_1","DOI":"10.1201\/9781420035933"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"crossref","DOI":"10.1137\/1.9781611970791","volume-title":"Interior-point polynomial algorithms in convex programming","author":"Nesterov Y.","year":"1994","unstructured":"Y. Nesterov , A. Nemirovskii , and Y. Ye . Interior-point polynomial algorithms in convex programming , volume 13 . SIAM , 1994 . Y. Nesterov, A. Nemirovskii, and Y. Ye. Interior-point polynomial algorithms in convex programming, volume 13. SIAM, 1994."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_21_1","DOI":"10.5555\/1390681.1442776"},{"key":"e_1_3_2_1_22_1","volume-title":"The annals of statistics, 6(2):461--464","author":"Schwarz G.","year":"1978","unstructured":"G. Schwarz . Estimating the dimension of a model. The annals of statistics, 6(2):461--464 , 1978 . G. Schwarz. Estimating the dimension of a model. The annals of statistics, 6(2):461--464, 1978."},{"key":"e_1_3_2_1_23_1","volume-title":"Causality from probability","author":"Spirtes P.","year":"1989","unstructured":"P. Spirtes , C. Glymour , and R. Scheines . Causality from probability . Carnegie-Mellon University , Laboratory for Computational Linguistics, 1989 . P. Spirtes, C. Glymour, and R. Scheines. Causality from probability. Carnegie-Mellon University, Laboratory for Computational Linguistics, 1989."},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_24_1","DOI":"10.1145\/1281192.1281270"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_25_1","DOI":"10.1145\/956750.956838"},{"doi-asserted-by":"publisher","key":"e_1_3_2_1_26_1","DOI":"10.1007\/s10994-006-6889-7"}],"event":{"sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"],"acronym":"KDD' 13","name":"KDD' 13: The 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","location":"Chicago Illinois"},"container-title":["Proceedings of the 2nd International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/2501221.2501226","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/2501221.2501226","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T07:28:47Z","timestamp":1750231727000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/2501221.2501226"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2013,8,11]]},"references-count":25,"alternative-id":["10.1145\/2501221.2501226","10.1145\/2501221"],"URL":"https:\/\/doi.org\/10.1145\/2501221.2501226","relation":{},"subject":[],"published":{"date-parts":[[2013,8,11]]},"assertion":[{"value":"2013-08-11","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}