{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T21:23:59Z","timestamp":1743110639663,"version":"3.40.3"},"publisher-location":"Boston, MA","reference-count":38,"publisher":"Springer US","isbn-type":[{"type":"print","value":"9780387307688"},{"type":"electronic","value":"9780387301648"}],"license":[{"start":{"date-parts":[[2011,1,1]],"date-time":"2011-01-01T00:00:00Z","timestamp":1293840000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2011,1,1]],"date-time":"2011-01-01T00:00:00Z","timestamp":1293840000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2011]]},"DOI":"10.1007\/978-0-387-30164-8_460","type":"book-chapter","created":{"date-parts":[[2010,12,29]],"date-time":"2010-12-29T17:28:18Z","timestamp":1293643698000},"page":"584-590","source":"Crossref","is-referenced-by-count":0,"title":["Learning Graphical Models"],"prefix":"10.1007","author":[{"given":"Kevin B.","family":"Korb","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","reference":[{"key":"460_CR1_460","unstructured":"The PC algorithm and variants were initially documented in Spirtes et\u00a0al.\u00a0(1993); their second edition (Spirtes, Glymour, & Scheines,\u00a02000) covers more ground. Their TETRAD IV program is available from their web site http:\/\/www.phil.cmu.edu\/projects\/tetrad\/. PC is contained within (and is available also with the weka machine learning platform at http:\/\/www.cs.waikato.ac.nz\/ml\/weka\/)."},{"key":"460_CR2_460","unstructured":"A well-known tutorial by David Heckerman\u00a0(1999) (reprinted without change in Heckerman,\u00a02008) is well worth looking at for background in causal discovery and parameterizing Bayesian networks. A more current review of many of the topics introduced here is to be found in a forthcoming article (Daly, Shen, & Aitken,\u00a0forthcoming). For other good treatments of parameterization see Cowell, Dawid, Lauritzen, and Spiegelhalter\u00a0(1999) or Neapolitan\u00a0(2003)."},{"key":"460_CR3_460","unstructured":"There are a number of useful anthologies in the area of learning graphical models. Learning in Graphical Models (Jordan,\u00a01999) is one of the best, including Heckerman\u2019s tutorial and a variety of excellent reviews of causal discovery methods, such as Markov Chain Monte Carlo search techniques."},{"key":"460_CR4_460","unstructured":"Textbooks treating the learning of Bayesian networks include Borgelt and Kruse\u00a0(2002), Neapolitan\u00a0(2003), Korb and Nicholson\u00a0(2004), and Koller and Friedman\u00a0(2009)."},{"key":"460_CR5_460","first-page":"171","volume":"11","author":"CF Aliferis","year":"2010","unstructured":"Aliferis, C. F., Statnikov, A., Tsamardinos, I., Mani, S., & Koutsoukos, X. D. (2010a). Local causal and Markov blanket induction for causal discovery and feature selection for classification. Part I: Algorithms and empirical evaluation. Journal of Machine Learning Research, 11, 171\u2013234.","journal-title":"Journal of Machine Learning Research"},{"key":"460_CR6_460","first-page":"171","volume":"11","author":"CF Aliferis","year":"2010","unstructured":"Aliferis, C. F., Statnikov, A., Tsamardinos, I., Mani, S., & Koutsoukos, X. D. (2010b). Local causal and Markov blanket induction for causal discovery and feature selection for classification. Part II: Analysis and extensions. Journal of Machine Learning Research, 11, 235\u2013284.","journal-title":"Journal of Machine Learning Research"},{"key":"460_CR7_460","volume-title":"Graphical models: Methods for data analysis and mining","author":"C Borgelt","year":"2002","unstructured":"Borgelt, C., & Kruse, R. (2002). Graphical models: Methods for data analysis and mining. New York: Wiley."},{"key":"460_CR8_460","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1007\/BFb0028180","volume":"747","author":"R Bouckaert","year":"1993","unstructured":"Bouckaert, R. (1993). Probabilistic network construction using the minimum description length principle. Lecture Notes in Computer Science, 747, 41\u201348.","journal-title":"Lecture Notes in Computer Science"},{"key":"460_CR9_460","first-page":"87","volume-title":"Proceedings of the 11th conference on uncertainty in artificial intelligence","author":"D. M Chickering","year":"1995","unstructured":"Chickering, D. M. (1995). A tranformational characterization of equivalent Bayesian network structures. In P. Besnard & S. Hanks (Eds.), Proceedings of the 11th conference on uncertainty in artificial intelligence, San Francisco (pp. 87\u201398)."},{"key":"460_CR10_460","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1023\/A:1007469629108","volume":"29","author":"DM Chickering","year":"1997","unstructured":"Chickering, D. M., & Heckerman, D. (1997). Efficient approximations for the marginal likelihood of Bayesian networks with hidden variables. Machine Learning, 29, 181\u2013212.","journal-title":"Machine Learning"},{"key":"460_CR11_460","first-page":"1287","volume":"5","author":"DM Chickering","year":"2004","unstructured":"Chickering, D. M., Heckerman, D., & Meek, C. (2004). Large-sample learning of Bayesian networks is NP-hard. Journal of Machine Learning Research, 5, 1287\u20131330.","journal-title":"Journal of Machine Learning Research"},{"key":"460_CR12_460","first-page":"94","volume-title":"Proceedings of the 18th annual conference on uncertainty in AI","author":"DM Chickering","year":"2002","unstructured":"Chickering, D. M., & Meek, C. (2002). Finding optimal Bayesian networks. In Proceedings of the 18th annual conference on uncertainty in AI, San Francisco (pp. 94\u2013102)."},{"key":"460_CR13_460","doi-asserted-by":"crossref","first-page":"462","DOI":"10.1109\/TIT.1968.1054142","volume":"14","author":"C Chow","year":"1968","unstructured":"Chow, C., & Liu, C. (1968). Approximating discrete probability distributions with dependence trees. IEEE Transactions on Information Theory, 14, 462\u2013467.","journal-title":"IEEE Transactions on Information Theory"},{"key":"460_CR14_460","volume-title":"Probabilistic networks and expert systems","author":"RG Cowell","year":"1999","unstructured":"Cowell, R. G., Dawid, A. P., Lauritzen, St. L., & Spiegelhalter, D. J. (1999). Probabilistic networks and expert systems. New York: Springer."},{"key":"460_CR15_460","doi-asserted-by":"crossref","first-page":"494","DOI":"10.1007\/11925231_46","volume":"4293","author":"N Cruz-Ram\u00edrez","year":"2006","unstructured":"Cruz-Ram\u00edrez, N., Acosta-Mesa, H. G., Barrientos-Mart\u00ednez, R. E., & Nava-Fern\u00e1ndez, L. A. (2006). How good are the Bayesian information criterion and the Minimum Description Length principle for model selection? A Bayesian network analysis. Lecture Notes in Computer Science, 4293, 494\u2013504.","journal-title":"Lecture Notes in Computer Science"},{"key":"460_CR16_460","unstructured":"Daly, R., Shen, Q., & Aitken, S. (forthcoming). Learning Bayesian networks: Approaches and issues. The Knowledge Engineering Review."},{"key":"460_CR17_460","first-page":"1177","volume":"5","author":"D Dash","year":"2004","unstructured":"Dash, D., & Cooper, G. F. (2004). Model averaging for prediction with discrete Bayesian networks. Journal of Machine Learning Research, 5, 1177\u20131203.","journal-title":"Journal of Machine Learning Research"},{"key":"460_CR18_460","unstructured":"Guyon, I., Aliferis, C., Cooper, G., Elisseeff, A., Pellet, J.-P., Spirtes, P., et\u00a0al. (Eds.) (2008). JMLR workshop and conference proceedings: Causation and prediction challenge (WCCI 2008), volume\u00a03. Journal of Machine Learning Research."},{"key":"460_CR19_460","first-page":"301","volume-title":"Learning in graphical models","author":"D Heckerman","year":"1999","unstructured":"Heckerman, D. (1999). A tutorial on learning with Bayesian networks. In M.\u00a0Jordan, (Ed.), Learning in graphical models (pp. 301\u2013354). Cambridge: MIT."},{"key":"460_CR20_460","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1007\/978-3-540-85066-3_3","volume-title":"Innovations in Bayesian networks","author":"D Heckerman","year":"2008","unstructured":"Heckerman, D. (2008). A tutorial on learning with Bayesian networks. In Innovations in Bayesian networks (pp. 33\u201382). Berlin: Springer Verlag."},{"key":"460_CR21_460","first-page":"293","volume-title":"Proceedings of the tenth conference on uncertainty in artificial intelligence","author":"D Heckerman","year":"1994","unstructured":"Heckerman, D., Geiger, D., & Chickering, D. M. (1994). Learning Bayesian networks: The combination of knowledge and statistical data. In Lopes de\u00a0Mantras & D. Poole (Eds.), Proceedings of the tenth conference on uncertainty in artificial intelligence, San Francisco (pp. 293\u2013301)."},{"key":"460_CR22_460","volume-title":"Learning in graphical models","author":"MI Jordan","year":"1999","unstructured":"Jordan, M. I. (1999). Learning in graphical models. Cambridge, MA: MIT."},{"key":"460_CR23_460","volume-title":"Probabilistic graphical models: Principles and techniques","author":"D Koller","year":"2009","unstructured":"Koller, D., & Friedman, N. (2009). Probabilistic graphical models: Principles and techniques. Cambridge, MA: MIT."},{"key":"460_CR24_460","volume-title":"Bayesian artificial intelligence","author":"KB Korb","year":"2004","unstructured":"Korb, K. B., & Nicholson, A. E. (2004). Bayesian artificial intelligence. 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