A formal approach to using data distributions for building causal polytree structures

Information Sciences

Volumn 168, Issue 1-4, 2004, Pages 111-132

  Ouerd, M ^a   Oommen, B J ^b   Matwin, S ^a  

^a UNIVERSITY OF OTTAWA   (Canada)

^b CARLETON UNIVERSITY   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; DATA REDUCTION; INFORMATION THEORY; INTEGRAL EQUATIONS; KNOWLEDGE BASED SYSTEMS; PROBLEM SOLVING; REAL TIME SYSTEMS; TREES (MATHEMATICS);

BAYESIAN NETWORKS; CAUSAL FUNCTIONS; FORMAL APPROACH; POLYTREE STRUCTURES;

FORMAL LOGIC;

EID: 9544257280     PISSN: 00200255     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ins.2004.01.001     Document Type: Article

References (24)

1. G.F. Cooper, E.H. Herskovits, A bayesian method for the induction of probabilistic networks from data, Machine Learning 9 (1992) 309-347.
2. C.K. Chow, C.N. Liu, Approximating discrete probability distributions with dependence trees, IEEE Transactions on Information Theory 14 (1968) 462-467.
3. S. Dasputa, Learning polytrees, in: Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence, Stockholm Sweden, July/August 1999, pp. 134-141. The paper can be downloaded from http://www2.sis.pitt.edu/~dsl/UAI/UAI99/Dasgupta.UAI99.html.
4. N. Friedman, The Bayesian structural EM algorithm, in: Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, University of Wisconsin Business School Madison, Wisconsin, USA, July 1998, pp. 129-138. The paper can be downloaded from http://www.cs.huji.ac.il/~nirf/Papers/Fr2.pdf.
5. N. Friedman, I. Nachman, D. Pe'er, Learning Bayesian network structure from massive datasets: the "Sparse Candidate" algorithm, in: Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence, Stockholm Sweden, July/August 1999, pp. 206-215. The paper can be downloaded from http://www2.sis.pitt.edu/~dsl/UAI/UAI99/Friedman.UAI99.html.
6. D. Geiger, A. Paz, J. Pearl, Learning causal trees from dependence information, in: Proceedings of 1990 AAAI Conference, Boston, MA, MIT Press, 1990, pp. 770-776.
7. E.H. Herskovits, Computer-based probabilistic-network construction, Doctoral Dissertation, Medical Information Sciences, Stanford University, CA, 1991.
8. S. Kullback, R.A. Leibler, On information and sufficiency, Annals Mathematics Statistics 22 (1951) 79-86.
9. T.M. Mitchell, Machine Learning, McGraw-Hill, 1997.
10. S. Dasputa, Learning probability distributions, Doctoral Dissertation, University of California at Berkeley, 2000.
11. M. Ouerd, B.J. Oommen, S. Matwin, Generation of random vectors for underlying DAG structures given first order marginals, in preparation.
12. M. Ouerd, B.J. Oommen, S. Matwin, Inferring polytree dependencies from sampled data, in: Proceedings of the 2002 IEEE International Conference on Systems, Man and Cybernetics, Hammamet, Tunisia, October 2002, in press.
13. M. Ouerd, Building probabilistic networks and its application to distributed databases, Doctoral Dissertation, SITE, University of Ottawa, Ottawa, Canada, 2000.
14. J. Pearl, Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, Morgan Kaufmann, San Mateo, CA, 1988.
15. G. Rebane, J. Pearl, The recovery of causal polytrees from statistical data, in: Proceedings of the Third Conference on Uncertainty in Artificial Intelligence, Seattle, Washington, July 1987, pp. 222-228.
16. S. Srinivas, S. Russell, A. Agogino, Automated construction of sparse bayesian networks from unstructured probabilistic models and domain information, in: Proceedings of the Fifth Workshop on Uncertainty in Artificial Intelligence, Windsor, Canada, August 1989, pp. 343-350. The paper can be downloaded from http://www2.sis.pitt.edu/~dsl/UAI/uai89.html.
17. R.S. Valiveti, B.J. Oommen, On using the chi-squared metric for determining stochastic dependence, Pattern Recognition 25 (11) (1992) 1389-1400.
18. T. Verma, J. Pearl, An algorithm for deciding if a set of observed independencies has a causal explanation, in: Proceedings of the Eighth Conference on Uncertainty in Artificial Intelligence, San Mateo, CA, July 1992, pp. 323-330. The paper can be downloaded from http://singapore.cs.ucla.edu/ csl_papers.html.
19. T. Verma, J. Pearl, Equivalence and synthesis of causal models, in: Proceedings of the Sixth Conference on Uncertainty in Artificial Intelligence, Cambridge, MA, July 1990, pp. 220-227.
20. S. Acid, L.M. de Campos, Approximations of causal networks by polytrees: an empirical study, in: B. Bouchon-Meunier, R.R. Yager, L.A. Zadeh (Eds.), Proceedings of the Fifth IPMU Conference, 972-977. Also found in Advances in Intelligent Computing, Springer Verlag, 1994, pp. 149-158.
21. S. Acid, L.M. de Campos, Approximations of causal networks by polytrees: an empirical study, in: B. Bouchon-Meunier, R.R. Yager, L.A. Zadeh (Eds.), Proceedings of the Fifth IPMU Conference, 972-977. Also found in Advances in Intelligent Computing, Springer Verlag, 1994, pp. 149-158.
22. J.F. Huete, L.M. de Campos, Learning causal polytrees, in: M. Clarke, R. Kruse, S. Moral (Eds.), Proceedings of the 1993 Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty, Lecture Notes in Computer Science, vol. 747, Springer Verlag, 1993, pp. 180-185.
23. A. Acid, L.M. de Campos, A. Gonzales, R. Molina, N. Perez de la Blanca, Learning with CASTLE, in: R. Kruse, P. Siegel (Eds.), Proceedings of the 1991 Conference on Symbolic and Quantitative Approaches to Uncertainty, Lecture Notes in Computer Science, vol. 548, Springer-Verlag, 1991, pp. 99-106.
24. M. Meila, T. Jaakkola, Tractable Bayesian learning of tree belief networks, in: Proceedings of the Sixteenth Workshop on Uncertainty in Artificial Intelligence. The paper can be downloaded from http://www.stat.washington.edu/ mmp/#publications.