Constructing probabilistic models

International Journal of Medical Informatics

Volumn 45, Issue 1-2, 1997, Pages 9-18

  Jiroušek, Radim ^a,b   Kushmerick, Nicholas ^b,c  

^a V E   (Czech Republic)

^b INSTITUTE OF INFORMATION THEORY AND AUTOMATION   (Czech Republic)

^c University of Washington   (United States)

Author keywords

Bayesian network; Graph models; Learning; Probability theory; Uncertainty

Indexed keywords

ALGORITHMS; ARTIFICIAL INTELLIGENCE; GRAPHIC METHODS; LEARNING SYSTEMS;

BAYESIAN NETWORKS;

PROBABILISTIC LOGICS;

ALGORITHM; ARTICLE; BAYES THEOREM; COMPUTER PROGRAM; EXPERT SYSTEM; MATHEMATICAL MODEL; MEDICAL INFORMATION; PRIORITY JOURNAL; SYSTEM ANALYSIS;

EID: 0343144835     PISSN: 13865056     EISSN: None     Source Type: Journal    
DOI: 10.1016/S1386-5056(97)00026-9     Document Type: Article

References (22)

1. R.R. Bouckaert, Probabilistic network construction using the minimum description length principle, in: Transactions of ESQARU'93, Granada, 1993, pp. 41-48.
2. P. Cheeseman, A method of computing generalized Bayesian probability values for expert systems, in: Proc. 6th Int. Conf. AI (IJCAI 83), Karlsruhe, FRG, 1983, pp. 198-202.
3. P. Cheeseman, In defense of probability, in: Proc. 8th Int. Conf. AI (IJCAI 85), Los Angeles, CA, 1985, pp. 1002-1007.
4. I. Csiszár, I-divergence geometry of probability distributions and minimization problems, Ann. Probab. 3 (1975) 146-158.
5. W.E. Deming, F.F. Stephan, On a least square adjustment of a sampled frequency table when the expected marginal totals are known, Ann. Math. Stat. 11 (1940) 427-444.
6. P. Hájek, T. Havránek, R. Jiroušek, Uncertain Information Processing in Expert Systems, CRC Press, Inc., Boca Raton, 1992.
7. D. Heckerman, J. Breese, J. Rommelse, Decision Theoretic Troubleshooting, Communication of the ACM, March 1995.
8. D. Heckerman, D. Geiger, D.M. Chickering, Learning Bayesian networks: the combination of knowledge and statistical data, Techn. Rep. MSR-TR-94-09, Microsoft Research, Advanced Technology Division, Microsoft Co., Redmond, WA, 1994.
9. F.V. Jensen, Introduction to Bayesian Network, UCL Press, 1995.
10. R. Jiroušek, Solution of the marginal problem and decomposable distributions, Kybernetika 27 (1991) 403-412.
11. R. Jiroušek, From learning Bayesian networks to a decomposable model construction, in: Papers and Abstracts from the Fifth Annual Workshop on Normative Systems, George Mason University, Fairfax, Virginia, 1995, pp. 8-10.
12. R. Jiroušek, G.D. Kleiter, A note on learning Bayesian networks, in: Y. Kodratoff, G. Nakhaeizadeh, C. Taylor (Eds.), 8th Eur. Conf. Machine Learning. Workshop notes: Statistics, Machine Learning and Knowledge Discovery in Databases, Heraklion, Greece, 1995, pp. 148-153.
13. R. Jiroušek, S. Přeučil, On the effective implementation of the iterative proportional fitting procedure, Comput. Stat. Data Anal. 19 (1995) 177-189.
14. N. Kushmerick, The Probabilistic Model Editor User's Manual. Technical Report, Institute of Information Theory and Automation, Academy of Science of the Czech Republic, Prague, 1995.
15. W. Lam, F. Bacchus, Learning Bayesian belief networks: an approach based on the MDL principle, Comput. Intell. 10 (1994) 269-293.
16. R.J.A. Little, D.B. Rubin, Statistical Analysis with Missing Data, Wiley, NY, 1987.
17. S.L. Lauritzen, D. Spiegelhalter, Local computations with probabilities on graphical structures and their applications to expert systems, J. R. Stat. Soc. Ser. B. 50 (1988) 157-189.
18. A. Perez, A probabilistic approach to the integration of partial knowledge for medical decisionmaking (in Czech), in: Proc. 1st Czechoslovak Congress of Biomed. Eng. (BMI 83), Mariánské Lázně, 1983, pp. 221-226.
19. R.D. Shachter, Evaluating influence diagrams, Oper. Res. 34 (1986) 871-882.
20. R.D. Shachter, Intelligent probabilistic inference, in: L.N. Kanal, J.F. Lemmer (Eds.), Uncertainty in Artificial Intelligence, North Holland, Amsterdam, 1986, pp. 371-382.
21. R.D. Shachter, Probabilistic inference and influence diagrams, Oper. Res. 36 (1988) 589-604.
22. R.E. Tarjan, M. Yannakakis, Simple linear-time algorithms to test chordality of graphs, test acyclidity of hypergraphs, and selectively reduce acyclic hypergraphs, SIAM J. Comput. 13 (1984) 566-591.