{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T00:44:34Z","timestamp":1784249074076,"version":"3.55.0"},"reference-count":25,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2014,8,17]],"date-time":"2014-08-17T00:00:00Z","timestamp":1408233600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Knowl Inf Syst"],"published-print":{"date-parts":[[2015,4]]},"DOI":"10.1007\/s10115-014-0775-6","type":"journal-article","created":{"date-parts":[[2014,8,16]],"date-time":"2014-08-16T06:21:28Z","timestamp":1408170088000},"page":"1-28","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Transfer learning for Bayesian discovery of multiple Bayesian networks"],"prefix":"10.1007","volume":"43","author":[{"given":"Diane","family":"Oyen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Terran","family":"Lane","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2014,8,17]]},"reference":[{"key":"775_CR1","volume-title":"Generalised hypergeometric series","author":"WN Bailey","year":"1935","unstructured":"Bailey WN (1935) Generalised hypergeometric series. University Press, Cambridge"},{"key":"775_CR2","doi-asserted-by":"crossref","unstructured":"Beinlich IA, Suermondt HJ, Chavez RM, Cooper GF (1989) The ALARM monitoring system: a case study with two probabilistic inference techniques for belief networks. In: Second European conference on artificial intelligence in medicine, vol 38, pp 247\u2013256","DOI":"10.1007\/978-3-642-93437-7_28"},{"key":"775_CR3","doi-asserted-by":"crossref","unstructured":"Bj\u00f6rklund A, Husfeldt T, Kaski P, Koivisto M (2007) Fourier meets M\u00f6bius: fast subset convolution. In: Proceedings of the thirty-ninth annual ACM symposium on theory of computing, ACM, pp 67\u201374","DOI":"10.1145\/1250790.1250801"},{"key":"775_CR4","doi-asserted-by":"crossref","unstructured":"Buntine W (1991) Theory refinement on Bayesian networks. In: Seventh conference on uncertainty in artificial intelligence, pp 52\u201360","DOI":"10.1016\/B978-1-55860-203-8.50010-3"},{"issue":"1","key":"775_CR5","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1023\/A:1007379606734","volume":"28","author":"R Caruana","year":"1997","unstructured":"Caruana R (1997) Multitask learning. Mach Learn 28(1):41\u201375","journal-title":"Mach Learn"},{"key":"775_CR6","first-page":"309","volume":"9","author":"GF Cooper","year":"1992","unstructured":"Cooper GF, Herskovits E (1992) A Bayesian method for the induction of probabilistic networks from data. Mach Learn 9:309\u2013347","journal-title":"Mach Learn"},{"key":"775_CR7","unstructured":"Cooper G, Yoo C (1999) Causal discovery from a mixture of experimental and observational data. In: Conference on uncertainty in artificial intelligence, pp 116\u2013125"},{"issue":"1","key":"775_CR8","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1023\/A:1020249912095","volume":"50","author":"N Friedman","year":"2003","unstructured":"Friedman N, Koller D (2003) Being Bayesian about network structure: a Bayesian approach to structure discovery in Bayesian networks. Mach Learn 50(1):95\u2013125","journal-title":"Mach Learn"},{"issue":"2\u20133","key":"775_CR9","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1007\/s10994-008-5057-7","volume":"71","author":"M Grzegorczyk","year":"2008","unstructured":"Grzegorczyk M, Husmeier D (2008) Improving the structure MCMC sampler for Bayesian networks by introducing a new edge reversal move. Mach Learn 71(2\u20133):265\u2013305","journal-title":"Mach Learn"},{"issue":"3","key":"775_CR10","first-page":"197","volume":"20","author":"D Heckerman","year":"1995","unstructured":"Heckerman D, Geiger D, Chickering DM (1995) Learning Bayesian networks: the combination of knowledge and statistical data. Mach Learn 20(3):197\u2013243","journal-title":"Mach Learn"},{"key":"775_CR11","unstructured":"Koivisto M (2006) Advances in exact Bayesian structure discovery in Bayesian networks. In: Twenty-second conference annual conference on uncertainty in artificial intelligence, pp 241\u2013248"},{"key":"775_CR12","first-page":"549","volume":"5","author":"M Koivisto","year":"2004","unstructured":"Koivisto M, Sood K (2004) Exact Bayesian structure discovery in Bayesian networks. J Mach Learn Res 5:549\u2013573","journal-title":"J Mach Learn Res"},{"key":"775_CR13","doi-asserted-by":"crossref","unstructured":"Lauritzen S, Spiegelhalter D (1988) Local computations with probabilities on graphical structures and their application to expert systems. J R Stat Soc B (Methodological) 50:157\u2013224","DOI":"10.1111\/j.2517-6161.1988.tb01721.x"},{"issue":"1\u20132","key":"775_CR14","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1007\/s10994-009-5160-4","volume":"79","author":"R Luis","year":"2010","unstructured":"Luis R, Sucar LE, Morales EF (2010) Inductive transfer for learning Bayesian networks. Mach Learn 79(1\u20132):227\u2013255","journal-title":"Mach Learn"},{"key":"775_CR15","doi-asserted-by":"crossref","unstructured":"Madigan D, York J, Allard D (1995) Bayesian graphical models for discrete data. Int Stat Rev 63(2):215\u2013232","DOI":"10.2307\/1403615"},{"key":"775_CR16","unstructured":"Niculescu-Mizil A, Caruana R (2007) Inductive transfer for Bayesian network structure learning. In: Eleventh international conference on artificial intelligence and statistics"},{"key":"775_CR17","unstructured":"Niinimaki T, Parviainen P, Koivisto M (2011) Partial order MCMC for structure discovery in Bayesian networks. In: Twenty-seventh annual conference on uncertainty in artificial intelligence, pp 557\u2013564"},{"key":"775_CR18","unstructured":"Oyen D, Lane T (2012) Leveraging domain knowledge in multitask Bayesian network structure learning. In: Twenty-Sixth AAAI conference on artificial intelligence"},{"key":"775_CR19","unstructured":"Parviainen P, Koivisto M (2009) Exact structure discovery in Bayesian networks with less space. In: Twenty-fifth conference on uncertainty in artificial intelligence, pp 436\u2013443"},{"key":"775_CR20","doi-asserted-by":"crossref","unstructured":"Pearl J, Bareinboim E (2011) Transportability of causal and statistical relations: a formal approach. In: Twenty-fifth national conference on artificial intelligence, pp 247\u2013254","DOI":"10.1109\/ICDMW.2011.169"},{"key":"775_CR21","unstructured":"Richardson M, Domingos P (2003) Learning with knowledge from multiple experts. In: ICML, vol 20, pp 624\u2013631"},{"key":"775_CR22","unstructured":"Thrun S (1996) Is learning the n-th thing any easier than learning the first? Adv Neural Inf Process Syst, 8:640\u2013646"},{"key":"775_CR23","unstructured":"Tong S, Koller D (2001) Active learning for structure in Bayesian networks. In: International joint conference on artificial intelligence, vol 17, pp 863\u2013869"},{"key":"775_CR24","doi-asserted-by":"crossref","unstructured":"Werhli A, Husmeier D (2007) Reconstructing gene regulatory networks with Bayesian networks by combining expression data with multiple sources of prior knowledge. Stat Appl Genet Mol Biol 6(1)","DOI":"10.2202\/1544-6115.1282"},{"key":"775_CR25","doi-asserted-by":"crossref","unstructured":"Zhang J, Zhang C (2010) Multitask Bregman clustering. In: Twenty-fourth national conference on artificial intelligence","DOI":"10.1609\/aaai.v24i1.7674"}],"container-title":["Knowledge and Information Systems"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10115-014-0775-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10115-014-0775-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10115-014-0775-6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,7,16]],"date-time":"2023-07-16T02:41:36Z","timestamp":1689475296000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10115-014-0775-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2014,8,17]]},"references-count":25,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2015,4]]}},"alternative-id":["775"],"URL":"https:\/\/doi.org\/10.1007\/s10115-014-0775-6","relation":{},"ISSN":["0219-1377","0219-3116"],"issn-type":[{"value":"0219-1377","type":"print"},{"value":"0219-3116","type":"electronic"}],"subject":[],"published":{"date-parts":[[2014,8,17]]}}}