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 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, 8:195210,
1996.
 3

J. Cheng, R. Greiner.
Comparing bayesian network classifiers.
Proceedings UAI,
101107,
1999.
 4

C.K. Chow, C.N.Liu.
Approximating discrete probability distributions with dependence trees.
IEEE Trans. on Info. Theory, IT14: 426467, 1968.
 5

G. Cooper, E. Herskovits.
A Bayesian method for the induction of probabilistic networks from data.
Machine Learning, 9: 309347, 1992.
 6

Cozman.
See http://www2.cs.cmu.edu/~fgcozman/Research/ InterchangeFormat/
for details on XML BIF.
 7

N. Friedman, D. Geiger, M. Goldszmidt.
Bayesian Network Classifiers.
Machine Learning, 29: 131163, 1997.
 8

D. Heckerman, D. Geiger, D. M. Chickering.
Learning Bayesian networks: the combination of knowledge and statistical data.
Machine Learining, 20(3): 197243, 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,
157224
 10

Moore, A. and Lee, M.S. Cached Sufficient Statistics for Efficient Machine Learning with Large Datasets,
JAIR,
Volume 8, pages 6791, 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,
323330,
1992.
 12

I.H. Witten, E. Frank.
Data Mining: Practical machine learning tools and techniques.
2nd Edition, Morgan Kaufmann, San Francisco, 2005.
Remco Bouckaert
20080512