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Learning Bayesian Networks: The Combination of Knowledge and Statistical Data
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Cites
Maximum Likelihood from Incomplete Data Via the EM Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444
Model Selection and Accounting for Model Uncertainty in Graphical Models Using Occam's Window
Madigan, D., & Raftery, A. E. (1994) · Journal of the American Statistical Association · doi:10.1080/01621459.1994.10476894 · Cited by 1,205 · cited 3×
“Other methods for escaping local maxima include best-first search (Korf, 1993) and Gibbs' sampling (e.g., Madigan & Raftery, 1994).”
BackgroundMethod
Research works printed but not linked: 49
- Aczel, J. (1966). Lectures on Functional Equations and Their Applications. Academic Press, New York.
- Beinlich, I., Suermondt, H., Chavez, R., & Cooper, G. (1989). The ALARM monitoring system: A case study with two probabilistic inference techniques for belief networks. In Proceedings of the Second European Conference on Artificial Intelligence in Medicine, London. Springer Verlag, Berlin.
- Buntine, W. (1991). Theory refinement on Bayesian networks. In Proceedings of Seventh Conference on Uncertainty in Artificial Intelligence, Los Angeles, CA, pages 52–60. Morgan Kaufmann. · DOI · Paper page
- Camerini, P. & Maffioli, L. F. F. (1980). The κ best spanning arborescences of a network. Networks, 10:91–110. · DOI
- Chickering, D. (1995a). A transformational characterization of equivalent Bayesian-network structures. In Proceedings of Eleventh Conference on Uncertainty in Artificial Intelligence, Montreal, QU, pages 87–98. Morgan Kaufmann.
- Chickering, D. (March, 1995b). Search operators for learning equivalence classes of Bayesian-network structures. Technical Report R231, Cognitive Systems Laboratory, University of California, Los Angeles.
- Chickering, D., Geiger, D., & Heckerman, D. (1995). Learning Bayesian networks: Search methods and experimental results. In Proceedings of Fifth Conference on Artificial Intelligence and Statistics, Ft. Lauderdale, FL, pages 112–128. Society for Artificial Intelligence in Statistics.
- Chow, C. & Liu, C. (1968). Approximating discrete probability distributions with dependence trees. IEEE Transactions on Information Theory, 14:462–467. · DOI · Paper page
- Cooper, G. & Herskovits, E. (1992). A Bayesian method for the induction of probabilistic networks from data. Machine Learning, 9:309–347. · doi:10.1023/a:1022649401552 · Paper page
- Dawid, A. & Lauritzen, S. (1993). Hyper Markov laws in the statistical analysis of decomposable graphical models. Annals of Statistics, 21:1272–1317.
- de Finetti, B. (1937). La prévision: See lois logiques, ses sources subjectives. Annales de l'Institut Henri Poincaré, 7:1–68. Translated in Kyburg and Smokler, 1964.
- Druzdzel, M. & Simon, H. (1993). Causality in Bayesian belief networks. In Proceedings of Ninth Conference on Uncertainty in Artificial Intelligence, Washington, DC, pages 3–11. Morgan Kaufmann. · DOI
- Edmonds, J. (1967). Optimum brachching. J. Res. NBS, 71B:233–240.
- Evans, J. & Minieka, E. (1991). Optimization algorithms for networks and graphs. Marcel Dekker Inc., New York.
- Gabow, H. (1977). Siam journal of computing. Networks, 6:139–150.
- Gabow, H., Galil, Z., & Spencer, T. (1984). Efficient implementation of graph algorithms using contraction. In Proceedings of FOCS. · DOI
- Geiger, D. & Heckerman, D. (1994). Learning Gaussian networks. In Proceedings of Tenth Conference on Uncertainty in Artificial Intelligence, Seattle, WA, pages 235–243. Morgan Kaufmann. · DOI · Paper page
- Geiger, D. & Heckerman, D. (1995). A characterization of the Dirichlet distribution with application to learning Bayesian networks. In Proceedings of Eleventh Conference on Uncertainty in Artificial Intelligence, Montreal, QU, pages 196–207. Morgan Kaufmann.
- Good, I. (1965). The Estimation of Probabilities. MIT Press, Cambridge, MA.
- Heckerman, D. (1995). A Bayesian approach for learning causal networks. In Proceedings of Eleventh Conference on Uncertainty in Artificial Intelligence, Montreal, QU, pages 285–295. Morgan Kaufmann.
- Heckerman, D. & Geiger, D. (1995). Learning Bayesian networks: A unification for discrete and Gaussian domains. In Proceedings of Eleventh Conference on Uncertainty in Artificial Intelligence, Montreal, QU, pages 274–284. Morgan Kaufmann.
- Heckerman, D., Geiger, D., & Chickering, D. (1994). Learning Bayesian networks: The combination of knowledge and statistical data. In Proceedings of Tenth Conference on Uncertainty in Artificial Intelligence, Seattle, WA, pages 293–301. Morgan Kaufmann. · DOI · Paper page
- Heckerman, D. & Nathwani, B. (1992). An evaluation of the diagnostic accuracy of Pathfinder. Computers and Biomedical Research, 25:56–74. · DOI
- Heckerman, D. & Shachter, R. (1995). A definition and graphical representation of causality. In Proceedings of Eleventh Conference on Uncertainty in Artificial Intelligence, Montreal, QU, pages 262–273. Morgan Kaufmann.
- Höffgen, K. (revised 1993). Learning and robust learning of product distributions. Technical Report 464, Fachbereich Informatik, Universität Dortmund. · DOI
- Horvitz, E. (1987). Reasoning about beliefs and actions under computational resource constraints. In Proceedings of the Third Workshop on Uncertainty in Artificial Intelligence, Seattle, WA. Association for Uncertainty in Artificial Intelligence, Mountain View, CA. Also in Kanal, L., Levitt, T., and Lemmer, J., editors, Uncertainty in Artificial Intelligence 3, pages 301–324. North-Holland, New York, 1989.
- Howard, R. (1988). Uncertainty about probability: A decision-analysis perspective. Risk Analysis, 8:91–98. · DOI
- Howard, R. & Matheson, J. (1981). Influence diagrams. In Howard, R. and Matheson, J., editors, Readings on the Principles and Applications of Decision Analysis, volume II, pages 721–762. Strategic Decisions Group, Menlo Park, CA.
- Johnson (1985). How fast is local search? In FOCS, pages 39–42. · DOI
- Karp, R. (1971). A simple derivation of Edmond's algorithm for optimal branchings. Networks, 1:265–272. · DOI
- Korf, R. (1993). Linear-space best-first search. Artificial Intelligence, 62:41–78. · DOI · Paper page
- Kullback, S. & Leibler, R. (1951). Information and sufficiency. Ann. Math. Statistics, 22:79–86. · DOI · Paper page
- Kyburg, H. & Smokler, H. (1964). Studies in Subjective Probability. Wiley and Sons, New York.
- Lam, W. & Bacchus, F. (1993). Using causal information and local measures to learn Bayesian networks. In Proceedings of Ninth Conference on Uncertainty in Artificial Intelligence, Washington, DC, pages 243–250. Morgan Kaufmann. · DOI
- Lauritzen, S. (1982). Lectures on Contingency Tables. University of Aalborg Press, Aalborg, Denmark.
- Matzkevich, I. & Abramson, B. (1993). Deriving a minimal I-map of a belief network relative to a target ordering of its nodes. In Proceedings of Ninth Conference on Uncertainty in Artificial Intelligence, Washington, DC, pages 159–165. Morgan Kaufmann. · DOI
- Metropolis, N., Rosenbluth, A., Rosenbluth, M., Teller, A., & Teller, E. (1953). Journal of Chemical Physics, 21:1087–1092. · DOI · Paper page
- Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Mateo, CA.
- Pearl, J. & Verma, T. (1991). A theory of inferred causation. In Allen, J., Fikes, R., and Sandewall, E., editors, Knowledge Representation and Reasoning: Proceedings of the Second International Conference, pages 441–452. Morgan Kaufmann, New York.
- Spiegelhalter, D., Dawid, A., Lauritzen, S., & Cowell, R. (1993). Bayesian analysis in expert systems. Statistical Science, 8:219–282.
- Spiegelhalter, D. & Lauritzen, S. (1990). Sequential updating of conditional probabilities on directed graphical structures. Networks, 20:579–605. · DOI · Paper page
- Spirtes, P., Glymour, C., & Scheines, R. (1993). Causation, Prediction, and Search. Springer-Verlag, New York. · DOI · Paper page
- Spirtes, P. & Meek, C. (1995). Learning Bayesian networks with discrete variables from data. In Proceedings of First International Conference on Knowledge Discovery and Data Mining, Montreal, QU. Morgan Kaufmann.
- Suzuki, J. (1993). A construction of Bayesian networks from databases based on an MDL scheme. In Proceedings of Ninth Conference on Uncertainty in Artificial Intelligence, Washington, DC, pages 266–273. Morgan Kaufmann. · DOI
- Tarjan, R. (1977). Finding optimal branchings. Networks, 7:25–35. · DOI · Paper page
- Titterington, D. (1976). Updating a diagnostic system using unconfirmed cases. Applied Statistics, 25:238–247. · DOI
- Verma, T. & Pearl, J. (1990). Equivalence and synthesis of causal models. In Proceedings of Sixth Conference on Uncertainty in Artificial Intelligence, Boston, MA, pages 220–227. Morgan Kaufmann.
- Winkler, R. (1967). The assessment of prior distributions in Bayesian analysis. American Statistical Association Journal, 62:776–800. · DOI · Paper page
- York, J. (1992). Bayesian methods for the analysis of misclassified or incomplete multivariate discrete data. PhD thesis, Department of Statistics, University of Washington, Seattle.
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Friedman, N., Geiger, D., & Goldszmidt, M. (1997) · Machine Learning · doi:10.1023/a:1007465528199 · Cited by 4,742
Self-citationHoeting, J. A., Madigan, D., Raftery, A. E., & Volinsky, C. T. (1999) · Statistical Science · doi:10.1214/ss/1009212519 · Cited by 4,118
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Metadata
- Authors
- Heckerman, D., Geiger, D., & Chickering, D. M.
- Venue
- Machine Learning
- Published
- 1995-09
- Volume
- 20
- Issue
- 3
- Pages
- 197-243
- ISSN
- 0885-6125 (ISSN-L)
- 1573-0565
- OpenAlex
- W2170112109
- Cited by
- 3,231
- References
- 2
- FWCI
- 46.92
- Type
- journal article
- Language
- en
- Open access
- bronze
- Licence
- www.springernature.com/gp/researchers/text-and-data-mining (tdm)
- www.springernature.com/gp/researchers/text-and-data-mining (vor)
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.