A new method for efficient symbolic propagation in discrete Bayesian networks

Networks

Volumn 28, Issue 1, 1996, Pages 31-43

  Castillo, Enrique ^b   Gutiérrez, José Manuel ^b   Hadi, Ali S ^a  

^a Department of Obstetrics Gynecology   (United States)

^b NONE

Author keywords

[No Author keywords available]

Indexed keywords

EID: 0030492812     PISSN: 00283045     EISSN: None     Source Type: Journal    
DOI: 10.1002/(SICI)1097-0037(199608)28:1<31::AID-NET5>3.3.CO;2-L     Document Type: Article

References (20)

1. R. R. Bouckaert, E. Castillo, and J. M. Gutiérrez, A modified simulation scheme for inference in Bayesian networks. Int. J. Approx. Reason. 20 (1995) 1-26.
2. E. Caslillo, J. M. Gutiérrez, and A. S. Hadi, Parametric structure of probabilities in Bayesian networks. Lectures Notes in Artificial Intelligence. Springer-Verlag, 946 (1995) 89-98.
3. E. Castillo, J. M. Gutiérrez, and A. S. Hadi, Symbolic propagation in discrete and continuous Bayesian networks. Mathematics with Vision: Proceedings of the First International Mathematica Symposium (V. Keranen and P. Mitic, Eds.). Computational Mechanics Publications, Southampton/Boston (1995) 77-84.
4. E. Castillo, J. M. Gutiérrez, and A. S. Hadi, Sensitivity Analysis in Discrete Bayesian Networks. Technical Report 95-10, Statistics Center, Cornell University (1995).
5. E. Castillo, J. M. Gutiérrez, and A. S. Hadi, Expert Systems and Probabilistic Network Models. Springer-Verlag, New York (1996).
6. R. Fung and K.-C. Chang, Weighing and integrating evidence for stochastic simulation in Bayesian networks. Uncertainty in Artificial Intelligence 5, Machine Intelligence and Pattern Recognition Series, 10 (M. Henrion, R. D. Shachter, L. N. Kanal, and J. F. Lemmer, Eds.). North-Holland, Amsterdam (1990) 209-219.
7. M. Henrion, Propagating uncertainty in Bayesian networks by probabilistic logic sampling. Uncertainty in Artificial Intelligence 2 (J. F. Lemmer and L. N. Kanal, Eds.). North-Holland, Amsterdam (1988) 317-324.
8. F. V. Jensen, K. G. Olesen, and S. K. Andersen, An algebra of Bayesian belief universes for knowledge-based systems. Networks 20 (1990) 637-659.
9. J. H. Kim and J. Pearl, A computation model for causal and diagnostic reasoning in inference systems. Proceedings of the 8th International Joint Conference on AI, Los Angeles (1983) 190-193.
10. S. L. Lauritzen and D. J. Spiegelhalter, Local computations with probabilities on graphical structures and their application to expert systems. J. R. Stat. Soc. (B) 50 (1988) 157-224.
11. Z. Li and B. D'Ambrosio, Efficient inference in Bayes nets as a combinatorial optimization problem. Int. J. Approx. Reason. 11 (1) (1994) 55-81.
12. Z. Li and B. D'Ambrosio, A framework for ordering composite beliefs in belief networks. IEEE Trans. Syst. Man Cybernet. 25 (2) (1995) 243-255.
13. B. Martos, Hyperbolic programming. Nov. Res. Log. Q. 32 (1964) 135-156.
14. J. Pearl, Evidential reasoning using stochastic simulation of causal models. Artif. Intell. 32 (1986) 245-287.
15. J. Pearl, Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Mateo, CA (1988).
16. D. Pool, Average-case analysis of a search algorithm for estimating prior and posterior probabilities in Bayesian networks with extreme probabilities. Proceedings of the 13th International Joint Conference on Artificial Intelligence 13 (1) (1993) 606-612.
17. E. Santos and S. E. Shimony, Belief updating by enumerating high-probability independence-based assignments. Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence. Morgan Kaufmann, San Francisco (1994) 506-513.
18. R. D. Shachter and M. A. Peot, Simulation approaches to general probabilistic inference on belief networks. Uncertainty in Artificial Intelligence 5, Machine Intelligence and Pattern Recognition Series, 10 (M. Henrion, R. D. Shachter, L. N. Kanal, and J. F. Lemmer, Eds.). North-Holland, Amsterdam (1990) 221-231.
19. R. D. Shachter, B. D'Ambrosio, and B. DelFabero, Symbolic probabilistic inference in belief networks. Proceedings Eighth National Conference on AI (1990) 126-131.
20. R. D. Shachter, S. K. Andersen, and P. Szolovits, Global conditioning for probabilistic inference in belief networks. Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence. Morgan Kaufmann, San Francisco (1994) 514-522.