next up previous contents
Next: About this document ... Up: Bayesian Network Classifiers in Previous: Future development   Contents

Bibliography

1
R.R. Bouckaert. Bayesian Belief Networks: from Construction to Inference. Ph.D. thesis, University of Utrecht, 1995.

2
W.L. Buntine. A guide to the literature on learning probabilistic networks from data. IEEE Transactions on Knowledge and Data Engineering, 8:195-210, 1996.

3
J. Cheng, R. Greiner. Comparing bayesian network classifiers. Proceedings UAI, 101-107, 1999.

4
C.K. Chow, C.N.Liu. Approximating discrete probability distributions with dependence trees. IEEE Trans. on Info. Theory, IT-14: 426-467, 1968.

5
G. Cooper, E. Herskovits. A Bayesian method for the induction of probabilistic networks from data. Machine Learning, 9: 309-347, 1992.

6
Cozman. See http://www-2.cs.cmu.edu/~fgcozman/Research/ InterchangeFormat/ for details on XML BIF.

7
N. Friedman, D. Geiger, M. Goldszmidt. Bayesian Network Classifiers. Machine Learning, 29: 131-163, 1997.

8
D. Heckerman, D. Geiger, D. M. Chickering. Learning Bayesian networks: the combination of knowledge and statistical data. Machine Learining, 20(3): 197-243, 1995.

9
S.L. Lauritzen and D.J. Spiegelhalter. Local Computations with Probabilities on graphical structures and their applications to expert systems (with discussion). Journal of the Royal Statistical Society B. 1988, 50, 157-224

10
Moore, A. and Lee, M.S. Cached Sufficient Statistics for Efficient Machine Learning with Large Datasets, JAIR, Volume 8, pages 67-91, 1998.

11
Verma, T. and Pearl, J.: An algorithm for deciding if a set of observed independencies has a causal explanation. Proc. of the Eighth Conference on Uncertainty in Artificial Intelligence, 323-330, 1992.

12
I.H. Witten, E. Frank. Data Mining: Practical machine learning tools and techniques. 2nd Edition, Morgan Kaufmann, San Francisco, 2005.



Remco Bouckaert 2008-05-12