Learning in Bayes Nets

  1. I.A. Beinlich, H.J. Suermondt, R.M. Chavez, G.F. Cooper, 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 Medical Care (Springer-Verlag, Berlin, 1989) 247-256.
  2. D.M. Chickering, Optimal Structure Identification with Greedy Search, Journal of Machine Learning Research 3 (2002) 507-554.
  3. G.F. Cooper, E. Herskovits, A Bayesian method for the induction of probabilistic networks from data, Machine Learning 9 (1992) 309-347.
  4. N. Friedman, I. Nachman, D. Pe'er., Learning Bayesian network structure from massive datasets: The "sparse candidate" algorithm, in: Proceedings of 15th Conference on Uncertainty in Artificial Intelligence (Stockholm, Sweden, 1999) 206-215.
  5. T.L. Griffiths, E.R. Baraff, J.B. Tenenbaum, Using physical theories to infer hidden causal structure, in: Proceedings of the 26th Annual Conference of the Cognitive Science Society (2004).
  6. David Heckerman, A Tutorial on Learning with Bayesian Networks. In Learning in Graphical Models, M. Jordan, ed. MIT Press, Cambridge, MA, 1999.
  7. D. Heckerman, D. Geiger, D.M. Chickering, Learning Bayesian networks: The combination of knowledge and statistical data, Machine Learning 20 (1995) 197-243.
  8. S.L. Lauritzen, D. Spiegelhalter, Local computations with prababilities on graphical structures and their application to expert systems, Journal of the Royal Statistical Society, Series B 50 (1988) 157-224.
  9. D. Madigan, J. York, Bayesian graphical models for discrete data, International statistical Review 63 (1995) 215-232.
  10. J. Pearl, T. Verma, A theory of inferred causation, in: J. Allen, R. Fikes, E. Sandewall (Eds.), Principles of Knowledge Representation and Reasoning: Proceeding of the Second International Conference (Morgan Kaufmann, San Mateo, CA, 1991) 441-452.
  11. S. Tong, D. Koller, Active Learning for Parameter Estimation in Bayesian Networks, in: T. Leen, T. Dietterich, V. Tresp (Eds.), Proceedings of the 13th Advances in Neural Information Processing Systems (NIPS) (MIT Press, 2000) 647-653.
  12. S. Tong, D. Koller, Active Learning for Structure in Bayesian Networks, in: B. Nebel (Ed.), Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence, IJCAI 2001 (Morgan Kaufmann, Seattle, Washington, USA, 2001) 863-869.
  13. I. Tsamardinos, C.F. Aliferis, A. Statnikov, Time and Sample Efficient Discovery of Markov Blankets and Direct Causal Relations, in: Proceedings of The 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2003) (2003) 673-678.

Back to home    last updated: 09/18/2007   © Copyright 2007