{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T01:09:49Z","timestamp":1785805789015,"version":"3.56.0"},"reference-count":29,"publisher":"Springer Science and Business Media LLC","issue":"1-2","license":[{"start":{"date-parts":[[2003,1,1]],"date-time":"2003-01-01T00:00:00Z","timestamp":1041379200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2003,1,1]],"date-time":"2003-01-01T00:00:00Z","timestamp":1041379200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Machine Learning"],"published-print":{"date-parts":[[2003,1]]},"DOI":"10.1023\/a:1020249912095","type":"journal-article","created":{"date-parts":[[2003,3,15]],"date-time":"2003-03-15T08:37:24Z","timestamp":1047717444000},"page":"95-125","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":521,"title":["Being Bayesian About Network Structure. A Bayesian Approach to Structure Discovery in Bayesian Networks"],"prefix":"10.1007","volume":"50","author":[{"given":"Nir","family":"Friedman","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daphne","family":"Koller","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","reference":[{"key":"5096739_CR1","volume-title":"Proc. 2nd European Conf. on AI and Medicine","author":"I. Beinlich","year":"1989","unstructured":"Beinlich, I., Suermondt, G., Chavez, R., &; Cooper, G. (1989). The ALARM monitoring system: A case study with two probabilistic inference techniques for belief networks. In Proc. 2nd European Conf. on AI and Medicine, Berlin: Springer-Verlag."},{"key":"5096739_CR2","first-page":"52","volume-title":"Proc. Seventh Annual Conference on Uncertainty Artificial Intelligence (UAI '91)","author":"W. L. Buntine","year":"1991","unstructured":"Buntine, W. L. (1991). Theory refinement on Bayesian networks. In B. D. D'Ambrosio, P. Smets, &; P. P. Bonissone (Eds.), Proc. Seventh Annual Conference on Uncertainty Artificial Intelligence (UAI '91) (pp. 52-60). San Francisco: Morgan Kaufmann."},{"key":"5096739_CR3","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1109\/69.494161","volume":"8","author":"W. L. Buntine","year":"1996","unstructured":"Buntine, W. L. (1996). A guide to the literature on learning probabilistic networks from data. IEEE Transactions on Knowledge and Data Engineering, 8, 195-210.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"issue":"1","key":"5096739_CR4","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1093\/biomet\/83.1.81","volume":"83","author":"G. Casella","year":"1996","unstructured":"Casella, G., &; Robert, C. (1996). Rao-Blackwellisation of sampling schemes. Biometrika, 83:1, 81-94.","journal-title":"Biometrika"},{"key":"5096739_CR5","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1023\/A:1022649401552","volume":"9","author":"G. F. Cooper","year":"1992","unstructured":"Cooper, G. F., &; Herskovits, E. (1992). A Bayesian method for the induction of probabilistic networks from data. Machine Learning, 9, 309-347.","journal-title":"Machine Learning"},{"key":"5096739_CR6","first-page":"206","volume-title":"Proc. Fifteenth Conference on Uncertainty in Artificial Intelligence (UAI '99)","author":"N. Friedman","year":"1999","unstructured":"Friedman, N., Goldszmidt, M., &; Wyner, A. (1999). Data analysis with Bayesian networks: A bootstrap approach. In Proc. Fifteenth Conference on Uncertainty in Artificial Intelligence (UAI '99) (pp. 206-215). San Francisco: Morgan Kaufmann."},{"key":"5096739_CR7","doi-asserted-by":"crossref","first-page":"601","DOI":"10.1089\/106652700750050961","volume":"7","author":"N. Friedman","year":"2000","unstructured":"Friedman, N., Linial, M., Nachman, I., &; Pe'er, D. (2000). Using Bayesian networks to analyze expression data. J. Computational Biology, 7, 601-620.","journal-title":"J. Computational Biology"},{"key":"5096739_CR8","doi-asserted-by":"crossref","first-page":"398","DOI":"10.1080\/01621459.1990.10476213","volume":"85","author":"A. Gelfand","year":"1990","unstructured":"Gelfand, A., &; Smith, A. (1990). Sampling based approaches to calculating marginal densities. Journal American Statistical Association, 85, 398-409.","journal-title":"Journal American Statistical Association"},{"key":"5096739_CR9","doi-asserted-by":"crossref","unstructured":"Gilks, W., Richardson, S., &; Spiegelhalter, D. (1996). Markov chain Monte Carlo methods in practice. CRC Press.","DOI":"10.1201\/b14835"},{"issue":"4","key":"5096739_CR10","doi-asserted-by":"crossref","first-page":"785","DOI":"10.1093\/biomet\/86.4.785","volume":"86","author":"P. Giudici","year":"1999","unstructured":"Giudici, P., &; Green, P. (1999). Decomposable graphical Gaussian model determination. Biometrika, 86:4, 785-801.","journal-title":"Biometrika"},{"key":"5096739_CR11","unstructured":"Giudici, P., Green, P., &; Tarantola, C. (2000). Efficient model determination for discrete graphical models. Discussion Paper 99-93, Department of Statistics, Athens University of Economics and Business."},{"key":"5096739_CR12","doi-asserted-by":"crossref","first-page":"711","DOI":"10.1093\/biomet\/82.4.711","volume":"82","author":"P. Green","year":"1995","unstructured":"Green, P. (1995). Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika, 82, 711-732.","journal-title":"Biometrika"},{"key":"5096739_CR13","volume-title":"Learning in graphical models","author":"D. Heckerman","year":"1998","unstructured":"Heckerman, D. (1998). A tutorial on learning with Bayesian networks. In M. I. Jordan (Ed.), Learning in graphical models. Dordrecht, The Netherlands: Kluwer."},{"key":"5096739_CR14","first-page":"274","volume-title":"Proc. Eleventh Conference on Uncertainty in Artificial Intelligence (UAI'95)","author":"D. Heckerman","year":"1995","unstructured":"Heckerman, D., &; Geiger, D. (1995). Learning Bayesian networks: A unification for discrete and Gaussian domains. In P. Besnard &; S. Hanks (Eds.), Proc. Eleventh Conference on Uncertainty in Artificial Intelligence (UAI'95) (pp. 274-284). San Francisco: Morgan Kaufmann."},{"key":"5096739_CR15","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1023\/A:1022623210503","volume":"20","author":"D. Heckerman","year":"1995","unstructured":"Heckerman, D., Geiger, D., &; Chickering, D. M. (1995). Learning Bayesian networks: The combination of knowledge and statistical data. Machine Learning, 20, 197-243.","journal-title":"Machine Learning"},{"key":"5096739_CR16","unstructured":"Heckerman, D., Meek, C., &; Cooper, G. (1997). A Bayesian approach to causal discovery. Technical Report MSR-TR-97-05, Microsoft Research."},{"issue":"1","key":"5096739_CR17","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1038\/4427","volume":"21","author":"E. Lander","year":"1999","unstructured":"Lander, E. (1999). Array of hope. Nature Genetics, 21:1, 3-4.","journal-title":"Nature Genetics"},{"issue":"4","key":"5096739_CR18","doi-asserted-by":"crossref","first-page":"487","DOI":"10.1109\/3468.508827","volume":"26","author":"P. Larra\u00f1aga","year":"1996","unstructured":"Larra\u00f1aga, P., Kuijpers, C., Murga, R., &; Yurramendi, Y. (1996). Learning Bayesian network structures by searching for the best ordering with genetic algorithms. IEEE Transactions on System, Man and Cybernetics 26:4, 487-493.","journal-title":"IEEE Transactions on System, Man and Cybernetics"},{"issue":"1","key":"5096739_CR19","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1093\/biomet\/81.1.27","volume":"81","author":"J. Liu","year":"1994","unstructured":"Liu, J., Wong, W., &; Kong, A. (1994). Coveriance structure of the Gibbs sampler with applications to the comparisons of estimators and augmentation schemes. Biometrika, 81:1, 27-40.","journal-title":"Biometrika"},{"key":"5096739_CR20","doi-asserted-by":"crossref","first-page":"2493","DOI":"10.1080\/03610929608831853","volume":"25","author":"D. Madigan","year":"1996","unstructured":"Madigan, D., Andersson, S., Perlman, M., &; Volinsky, C. (1996). Bayesian model averaging and model selection for Markov equivalence classes of acyclic graphs. Communications in Statistics: Theory and Methods, 25, 2493-2519.","journal-title":"Communications in Statistics: Theory and Methods"},{"key":"5096739_CR21","doi-asserted-by":"crossref","first-page":"1535","DOI":"10.1080\/01621459.1994.10476894","volume":"89","author":"D. Madigan","year":"1994","unstructured":"Madigan, D., &; Raftery, E. (1994). Model selection and accounting for model uncertainty in graphical models using Occam's window. Journal Americal Statistical Association, 89, 1535-1546.","journal-title":"Journal Americal Statistical Association"},{"key":"5096739_CR22","doi-asserted-by":"crossref","first-page":"215","DOI":"10.2307\/1403615","volume":"63","author":"D. Madigan","year":"1995","unstructured":"Madigan, D., &; York, J. (1995). Bayesian graphical models for discrete data. International Statistical Review, 63, 215-232.","journal-title":"International Statistical Review"},{"key":"5096739_CR23","doi-asserted-by":"crossref","first-page":"1087","DOI":"10.1063\/1.1699114","volume":"21","author":"N. Metropolis","year":"1953","unstructured":"Metropolis, N., Rosenbluth, A., Rosenbluth, M., Teller, A., &; Teller, E. (1953). Equation of state calculation by fast computing machines. Journal of Chemical Physics, 21, 1087-1092.","journal-title":"Journal of Chemical Physics"},{"key":"5096739_CR24","unstructured":"Murphy, P. M., &; Aha, D. W. (1995). UCI repository of machine learning databases. Available at http:\/\/www.ics.uci.edu\/~mlearn\/MLRepository.html."},{"key":"5096739_CR25","volume-title":"Probabilistic reasoning in intelligent systems","author":"J. Pearl","year":"1988","unstructured":"Pearl, J. (1988). Probabilistic reasoning in intelligent systems. San Francisco, CA: Morgan Kaufmann."},{"issue":"3","key":"5096739_CR26","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1023\/A:1007670818503","volume":"36","author":"F. Pereira","year":"1999","unstructured":"Pereira, F., &; Singer, Y. (1999). An efficient extension to mixture techniques for prediction and decision trees. Machine Learning, 36:3, 183-199.","journal-title":"Machine Learning"},{"key":"5096739_CR27","doi-asserted-by":"crossref","first-page":"3273","DOI":"10.1091\/mbc.9.12.3273","volume":"9","author":"P. Spellman","year":"1998","unstructured":"Spellman, P., Sherlock, G., Zhang, M., Iyer, V., Anders, K., Eisen, M., Brown, P., Botstein, D., &; Futcher (1998). Comprehensive identification of cell cycle-regulated genes of the yeastSaccharomyces cerevisiae by microarray hybridization. Molecular Biology of the Cell, 9, 3273-3297.","journal-title":"Molecular Biology of the Cell"},{"key":"5096739_CR28","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. (1993). Causation, prediction and search, Vol. 81 of Lecture Notes in Statistics. New York: Springer-Verlag."},{"key":"5096739_CR29","unstructured":"Wallace, C., Korb, K., &; Dai, H. (1996). Causal discovery via MML. In Proc. 13th International Conference on Machine Learning (pp. 516-524)."}],"container-title":["Machine Learning"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1023\/A:1020249912095.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1023\/A:1020249912095\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1023\/A:1020249912095.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,10]],"date-time":"2025-07-10T11:43:37Z","timestamp":1752147817000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1023\/A:1020249912095"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2003,1]]},"references-count":29,"journal-issue":{"issue":"1-2","published-print":{"date-parts":[[2003,1]]}},"alternative-id":["5096739"],"URL":"https:\/\/doi.org\/10.1023\/a:1020249912095","relation":{},"ISSN":["0885-6125","1573-0565"],"issn-type":[{"value":"0885-6125","type":"print"},{"value":"1573-0565","type":"electronic"}],"subject":[],"published":{"date-parts":[[2003,1]]},"assertion":[{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}