Optimal structure identification with greedy search

Journal of Machine Learning Research

Volumn 3, Issue 3, 2003, Pages 507-554

  Chickering, David Maxwell ^a  

^a MICROSOFT RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; DATA REDUCTION; OPTIMAL SYSTEMS; REAL TIME SYSTEMS;

OPTIMAL STRUCTURE IDENTIFICATION;

LEARNING SYSTEMS;

EID: 0042967741     PISSN: 15324435     EISSN: None     Source Type: Journal    
DOI: 10.1162/153244303321897717     Document Type: Conference Paper

References (22)

1. Andersson, S. A., Madigan, D., and Perlman, M. D. (1997). A characterization of Markov equivalence classes for acyclic digraphs. Annals of Statistics, 25:505-541.
2. 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.
3. Chickering, D. M. (1995). A transformational characterization of Bayesian network structures. In Hanks, S. and Besnard, P., editors, Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence, pages 87-98. Morgan Kaufmann.
4. Chickering, D. M. (1996). Learning Bayesian networks is NP-Complete. In Fisher, D. and Lenz, H., editors, Learning from Data: Artificial Intelligence and Statistics V, pages 121-130. Springer-Verlag.
5. Chickering, D. M. (2002). Learning equivalence classes of Bayesian-network structures. Journal of Machine Learning Research, 2:445-498.
6. Chickering, D. M. and Meek, C. (2002). Finding optimal Bayesian networks. In Darwiche, A. and Friedman, N., editors, Proceedings of the Eighteenth Conference on Uncertainty in Artificial Intelligence, pages 94-102. Morgan Kaufmann.
7. Cooper, G. F. and Herskovits, E. (1992). A Bayesian method for the induction of probabilistic networks from data. Machine Learning, 9:309-347.
8. Dor, D. and Tarsi, M. (1992). A simple algorithm to construct a consistent extension of a partially oriented graph. Technical Report R-185, Cognitive Systems Laboratory, UCLA Computer Science Department.
9. Geiger, D., Heckerman, D., King, H., and Meek, C. (2001). Stratified exponential families: graphical models and model selection. Annals of Statistics, 29(2):505-529.
10. Gillispie, S. B. and Perlman, M. D. (2001). Enumerating Markov equivalence classes of acyclic digraph models. In Goldszmidt, M., Breese, J., and Koller, D., editors, Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence, pages 171-177. Morgan Kaufmann.
11. Haughton, D. M. A. (1988). On the choice of a model to fit data from an exponential family. The Annals of Statistics, 16(1):342-355.
12. Heckerman, D. (1996). A tutorial on learning Bayesian networks. Technical Report MSR-TR-95-06, Microsoft Research.
13. Heckerman, D., Geiger, D., and Chickering, D. (1995). Learning Bayesian networks: The combination of knowledge and statistical data. Machine Learning, 20:197-243.
14. Jeffreys, H. (1939). Theory of Probability. Oxford University Press.
15. Kočka, T., Bouckaert, R. R., and Studený, M. (2001a). On characterizing inclusion of Bayesian networks. In Breese, J. and Koller, D., editors, Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence, pages 261-268. Morgan Kaufmann.
16. Kočka, T., Bouckaert, R. R., and Studený, M. (2001b). On the inclusion problem. Technical Report 2010, Academy of Sciences of the Czech Republic, Institute of Information Theory and Automation.
17. Meek, C. (1995). Causal inference and causal explanation with background knowledge. In Hanks, S. and Besnard, P., editors, Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence, pages 403-410. Morgan Kaufmann.
18. Meek, C. (1997). Graphical Models: Selecting causal and statistical models. PhD thesis, Carnegie Mellon University.
19. Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Mateo, CA.
20. Shachter, R. (1998). Bayes-ball: The rational pastime (for determining irrelevance and requisite information in belief networks and influence diagrams). In Cooper, G. and Moral, S., editors, Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, pages 480-487. Morgan Kaufmann.
21. Spirtes, P., Glymour, C., and Schemes, R. (1993). Causation, Prediction, and Search. Springer-Verlag, New York.
22. Verma, T. and Pearl, J. (1991). Equivalence and synthesis of causal models. In Henrion, M., Shachter, R., Kanal, L., and Lemmer, J., editors, Proceedings of the Sixth Conference on Uncertainty in Artificial Intelligence, pages 220-227.