References

• K2
  • G. F. Cooper and E. Herskovits. (1992). A Bayesian Method for the Induction of Probabilistic Networks from Data. Machine Learning, 9(4):309-347.
• LS
  • S. L. Lauritzen and D. J. Spiegelhalter. (1988). Local computations with probabilities on graphical structures and their application to expert systems. Journal of the Royal Statistical Society, Series B 50.
• Stochastic Sampling
  • Cheng, J. and Druzdzel, M. (2000). AIS-BN: An adaptive importance sampling algorithm for evidential reasoning in large Bayesian networks. Journal of Artificial Intelligence Research, 13, 155-188.
  • Dagum, P. and Luby, M. (1997). An optimal approximation algorithm for Bayesian inference. Artificial Intelligence, 93, 1-27.
  • Fung, R. and Chang, K.-C. (1989). Weighing and integrating evidence for stochastic simulation in Bayesian networks. In Uncertainty in Artificial Intelligence 5, pp. 209-219 New York, N.Y. Elsevier Science Publishing Company, Inc.
  • Henrion, M. (1988). Propagating uncertainty in Bayesian networks by probabilistic logic sampling. In Uncertainty in Artificial Intelligence 2, pp. 149-163 New York, N.Y. Elsevier Science Publishing Company, Inc.
  • Shachter, R., and Peot, M. (1989). Simulation approaches to general probabilistic inference on belief networks. In Uncertainty in Artificial Intelligence 5, pp. 221-231 New York, N.Y. Elsevier Science Publishing Company, Inc.
• Glue
  • H. Guo, B. B. Perry, J. A. Stilson, and W. H. Hsu. A Genetic Algorithm for Tuning Variable Orderings in Bayesian Network Structure Learning. AAAI02 Student Abstract, to appear.
  • W. H. Hsu, H. Guo, B. B. Perry, and J. A. Stilson. A Permutation Genetic Algorithm for Variable Ordering in Learning Bayesian Networks from Data. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2002), New York, NY, 2002, to appear.