Interactive structural learning of Bayesian networks

Expert Systems with Applications

Volumn 15, Issue 3-4, 1998, Pages 325-332

  Sucar, Luis Enrique ^a   Martínez Arroyo, Miriam ^b  

^a SCHOOL OF ENGINEERING AND SCIENCES   (Mexico)

^b Programa de Ingeniería Bioquímica   (Mexico)

Author keywords

Bayesian networks; Learning; MDL

Indexed keywords

EID: 0348117206     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/s0957-4174(98)00050-5     Document Type: Article

References (15)

1. Chow C. K., & Liu C. N. (1968). Approximating probability distributions with dependence trees. IEEE Transactions on Information Theory, 14 (3), 462-468.
2. Cooper G. F. (1990). The computational complexity of probabilistic inference using Bayesian networks. Artificial Intelligence, 42, 393-405.
3. Cooper G. F., & Herskovits E. (1992). A Bayesian method for the induction of probabilistic networks from data. Machine Learning, 9, 309-347.
4. Kim, J. & Pearl, J. (1983). A computational model for combined causal and diagnostic reasoning in inference systems. In Proceedings IJCAI, pp. 190-193.
5. Kwoh C. K., & Gillies D. F. (1997). Using hidden nodes in Bayesian networks. Artificial Intelligence, 88, 1-38.
6. Lam W., & Bacchus F. (1994). Learning Bayesian networks. An approach based on the MDL principle. Computational Intelligence, 10 (2), 269-293.
7. Lauritzen S. L., & Spiegelhalter D. J. (1988). Local computations with probabilities on graphical structures and their application to expert systems. Journal Royal Statistical Society B, 50 (2), 157-224.
8. Ng K. C., & Abramson B. (1990). Uncertainty management in expert systems. IEEE Expert, 5 (2), 29-48.
9. Pearl J. (1986). On evidential reasoning on a hierarchy of hypothesis. Artificial Intelligence, 28, 9-15.
10. Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems. San Mateo, CA: Morgan-Kaufmann.
11. Rebane, G. & Pearl, J. (1989). The recovery of causal poly-trees from statistical data. In L. N. Kanal, T. S. Levitt & J. F. Lemmer (Eds.), Uncertainty in Artificial Intelligence 3 (pp. 175-182). Amsterdam: North-Holland.
12. Simon, H. A. (1983). Why machines should learn? In R. S. Michalski, T. M. Mitchell & J. Carbonell (Eds.), Machine Learning (pp. 25-37). Los Altos, CA: Morgan-Kaufmann.
13. Srinivas, S., Russell, S. & Agogino, A. (1990). Automated construction of sparse Bayesian networks from unstructured probabilistic models and domain information. In M. Henrion, R. D. Shachter, L. N. Kanal, & J. F. Lemmer (Eds.), Uncertainty in Artificial Intelligence 5 (pp. 295-308). Amsterdam: North-Holland.
14. Sucar L. E., Gillies D. F., & Gillies D. A. (1993). Objective probabilities in expert systems. Artificial Intelligence, 61, 187-208.
15. Sucar L. E., Pérez-Brito J., Ruiz-Suárez J. C., & Morales E. (1997). Learning structure from data and its application to ozone prediction. Applied Intelligence, 7 (4), 327-338.