Abstract
In recent years, researchers in statistics and the UAI community have developed an impressive body of theory and algorithmic machinery for learning Bayesian networks from data. Learned Bayesian networks can be used for pattern discovery, prediction, diagnosis, and density estimation tasks. Early pioneering work in this area includes [5, 9, 10, 13]. The algorithm that has emerged as the current most popular approach is a simple greedy hill-climbing algorithm that searches the space of candidate structures, guided by a network scoring function (either Bayesian or Minimum Description Length (MDL)-based). The search begins with an initial candidate network (typically the empty network, which has no edges), and then considers making small local changes such as adding, deleting, or reversing an edge in the network.
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References
M.A. Behr, M.A. Wilson, W.P. Gill, H. Salamon, G.K. Schoolnik, S. Rane, and P.M. Small. Comparative genomics of BCG vaccines by whole genome DNA microarray. Science, 284:1520–23, 1999.
Craig Boutilier, Nir Friedman, Moises Goldszmidt, and Daphne Koller. Context-specific independence in Bayesian networks. In Proceedings of the Twelfth Conference on Uncertainty in Artificial Intelligence (UAI-96), pages 115–123, August 1996.
D. M. Chickering. Learning Bayesian networks is NP-complete. In D. Fisher and H.-J. Lenz, editors, Learning from Data: Artificial Intelligence and Statistics V Springer Verlag, 1996.
D. M. Chickering, D. Heckerman, and C. Meek. A Bayesian approach to learning Bayesian networks with local structure. In Proceedings of the Thirteenth Conference on Uncertainty in Artificial Intelligence (UAI-97), pages 80–89, 1997.
G. F. Cooper and E. Herskovits. A Bayesian method for the induction of probabilistic networks from data. Machine Learning, l9:309–347, 1992.
Nir Friedman and Moises Goldszmidt. Discretizing continuous attributes while learning Bayesian networks. In Proceedings of the Thirteenth International Conference on Machine Learning, 1996.
Nir Friedman and Moises Goldszmidt. Learning Bayesian networks with local structure. In Proceedings of the Twelfth Conference on Uncertainty in Artificial Intelligence (UAI-96), 1996.
D. Heckerman. A tutorial on learning with Bayesian networks. In M. I. Jordan, editor, Learning in Graphical Models Kluwer, Dordrecht, Netherlands, 1998.
D. Heckerman, D. Geiger, and D. M. Chickering. Learning Bayesian networks: The combination of knowledge and statistical data. Machine Learning, 20:197–243, 1995.
W. Lam and F. Bacchus. Learning Bayesian belief networks: An approach based on the MDL principle. Computational Intelligence, 10:269–293, 1994.
Stefano Monti and Gregory F. Cooper. A latent variable model for multivariate discretization. In Proceedings of the Seventh International Workshop on AI & Statistics (Uncertainty 99), 1999.
J. Pearl. Probabilistic Reasoning in Intelligent Systems Morgan Kaufmann, San Francisco, 1988.
D. J. Spiegelhalter, A. P. Dawid, S. L. Lauritzen, and R. G. Cowell. Bayesian analysis in expert systems. Statistical Science, 8:219–283, 1993.
P. Spirtes, C. Glymour, and R. Scheines. Causation, Prediction and Search Number 81 in Lecture Notes in Statistics. Springer-Verlag, NY, 1993.
Michael P. Wellman and Chao-Lin Liu. State-space abstraction for anytime evaluation of probabilistic networks. In Proceedings of the Ninth Conference on Uncertainty in Artificial Intelligence (UAI-93), pages 567–574, 1994.
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desJardins, M., Getoor, L., Koller, D. (2000). Using Feature Hierarchies in Bayesian Network Learning. In: Choueiry, B.Y., Walsh, T. (eds) Abstraction, Reformulation, and Approximation. SARA 2000. Lecture Notes in Computer Science(), vol 1864. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44914-0_16
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DOI: https://doi.org/10.1007/3-540-44914-0_16
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