Learning Bayesian networks. I. A theory based on MAP-MDL criteria

Proceedings of the 5th International Conference on Information Fusion, FUSION 2002

Volumn 2, Issue , 2002, Pages 769-776

  Pan, Heping ^a  

^a WUHAN UNIVERSITY   (China)

Author keywords

Bayesian networks; EM algorithm; Learning; maximum aposterior probability (MAP); minimum description length (MDL)

Indexed keywords

ALGORITHMS; INFORMATION FUSION; MAXIMUM LIKELIHOOD ESTIMATION; PROBABILITY DISTRIBUTIONS;

BAYESIAN NETWORK MODELS; COMPUTATIONAL ALGORITHM; EM ALGORITHMS; LEARNING; LEARNING BAYESIAN NETWORKS; MAXIMUM APOSTERIOR PROBABILITY (MAP); MINIMUM DESCRIPTION LENGTH; THEORY OF LEARNING;

BAYESIAN NETWORKS;

EID: 17744368925     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICIF.2002.1020884     Document Type: Conference Paper

References (15)

1. D. Heckerraan, "A tutorial on learning with Bayesian networks (revised)," Tech. Rep. MSR- TR-95-06, Microsoft Research, Advanced Technology Division, Microsoft Corporation, 1 Microsoft Way, Redmond, WA 98052, USA, March 1996.
2. W. Buntine, "A guide to the literature on learning probabilistic networks from data," IEEE Trans. Knowledge and Data Engineering, vol. 8, no. 2, 1996.
3. P. J. Krause, "Learning probabilistic networks," in http://mmo. auai. org/auai-tutes. html, 1998.
4. M. I. Jordan, Learning in Graphical Models. The Netherland: Kulwer Academic Publishers, 1998.
5. H.-P. Pan, "Fuzzy Bayesian networks - a general formalism for representation, inference and learning with hybrid Bayesian networks," International Journal of Pattern Recognition and Artificial Intelligence, vol. 14, no. 7, pp. 941-962, 2000.
6. G. Cooper and E. Herskovits, "A Bayesian method for the induction of probabilistic networks from data," Machine Learning, vol. 9, pp. 309347, 1992.
7. D. Geiger and D. Heckerman, "A characterization of the Dirichlet distribution with application to learning Bayesian networks," in Proc. Eleventh Conference on Uncertainty in Artificial Intelli- gence(UAI'95), 1995.
8. J. Rissanen, Stochastic Complexity in Statistical Inquiry. River Edge, NJ: World Scientific, 1989.
9. J. Suzuki, "A construction of Bayesian networks from databases based on an MDL scheme," in Proc. Ninth Conference on Uncertainty in Artificial Intelligence(UAI'93 (D. Heckerman and A. Mamdani, eds. ), (San Francisco), Morgan Kaufmann, 1993.
10. W. Lam and F. Bacchus, "Learning Bayesian belief networks: An approach based on the MDL principle," Computational Intelligence, vol. 10, pp. 269-293,1994.
11. R. Bouckaert, "Properties of Bayesian network learning algorithms," in Proc, Tenth Conference on Uncertainty in Artificial Intelligence(UAI'94) (R. L. de Mantaras and D. Poole, eds. ), (San Francisco), Morgan Kaufmann, 1994.
12. N. Friedman, "The Bayesian structural EM algorithm," in Proc. Fourteenth conference on Uncertainty in Artificial Intelligence(UAI'98) (G. Cooper and S. Moral, eds. ), (San Francisco), Morgan Kaufmann, 1998.
13. J. Rissanen, "Modelling by shortest data description," Automatic, vol. 14, pp. 465-471, 1978.
14. H.-P. Pan and W. Forstner, "An MDL-principled evolutionary mechanism to automatic architec- turing of pattern recognition neural network," in IEEE Proc. 11th Int. Conference of Int. Association of Pattern Recognition(IAPR), 1992.
15. H.-P. Pan, "Learning Bayesian networks: II - a computational algorithm," in 5th International Conference on Information Fusion, submitted, (Anapolis, Maryland, USA), 2002.