Independence of causal influence and clique tree propagation

International Journal of Approximate Reasoning

Volumn 19, Issue 3-4, 1998, Pages 335-349

  Zhang, Nevin Lianwen ^a   Yan, Li ^a  

^a HONG KONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Hong Kong)

Author keywords

Bayesian networks; Clique tree propagation; Independence of causal influence (causal independence); Inference

Indexed keywords

ALGORITHMS; GRAPH THEORY; KNOWLEDGE BASED SYSTEMS; PROBABILITY; RANDOM PROCESSES;

BAYESIAN NETWORKS; CLIQUE TREE PROPAGATION; INDEPENDENCE OF CAUSAL INFLUENCE; INFERENCE;

KNOWLEDGE REPRESENTATION;

EID: 0032178439     PISSN: 0888613X     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0888-613X(98)10014-2     Document Type: Article

References (23)

1. C. Boutillier, N. Friedman, M. Goldszmidt, D. Koller, Context-specific independence in Bayesian networks, in: Proceedings of the Twelfth Conference on Uncertainty in Artificial Intelligence, 1996, pp. 115-123.
2. R.M. Chavez, G.F. Cooper, A randomized approximation algorithm for probabilistic inference on Bayesian belief networks, Networks 20 (1990) 661-685.
3. D. Heckerman, Causal independence for knowledge acquisition and inference, in: Proceedings of the Ninth Conference on Uncertainty in Artificial Intelligence, 1993, pp. 122-127.
4. D. Heckerman, J. Breese, A new look at causal independence, in: Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence, 1994, pp. 286-292.
5. M. Henrion, Some practical issues in constructing belief networks, in: L. Kanal, T. Levitt, J. Lemmer (Eds.), Uncertainty in Artificial Intelligence, vol. 3, North-Holland, Amsterdam, 1987, pp. 161-174.
6. Henrion, Search-based methods to bound diagnostic probabilities in very large belief networks, in: Proceedings of the Seventh Conference on Uncertainty in Artificial Intelligence, 1991, pp. 142-150.
7. E.J. Horvitz, A.C. Klein, Utility-based abstraction and categorization, in: Proceedings of the Ninth Conference on Uncertainty in Artificial Intelligence, 1993, pp. 128-135.
8. R.A. Howard, J.E. Matheson, Influence diagrams, in: R.A. Howard, J.E. Matheson (Eds.), The Principles and Applications of Decision Analysis, vol. II, Strategic Decisions Group, Menlo Park, CA, USA, 1984.
9. F. Jensen, S.K. Andersen, Approximations in Bayesian belief universes for knowledge-based systems, in: Proceedings of the Sixth Conference on Uncertainty in Artificial Intelligence, Cambridge, MA, 1990, pp. 162-169.
10. F.V. Jensen, K.G. Olesen, K. Anderson, An algebra of Bayesian belief universes for knowledge-based systems, Networks 20 (1990) 637-659.
11. U. Kjærulff, Reduction of computational complexity in Bayesian networks through removal of weak dependences, in: Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence, 1994, pp. 374-382.
12. S.L. Lauritzen, D.J. Spiegelhalter, Local computations with probabilities on graphical structures and their applications to expert systems, Journal of Royal Statistical Society B 50 (2) (1988) 157-224.
13. K.G. Olesen, U. Kjærulff, F. Jensen, B. Falck, S. Andreassen, S.K. Andersen, A MUNIN network for the median nerve - A case study on loops, Applied Artificial Intelligence 3 (1989) 384-403.
14. K.G. Olesen, S. Andreassen, Specification of models in large expert systems based on causal probabilistic networks, Artificial Intelligence in Medicine 5 (1993) 269-281.
15. J. Pearl, Evidential reasoning using stochastic simulation of causal models, Artificial Intelligence 32 (1987) 245-257.
16. J. Pearl, Probabilistic Reasoning in Intelligence Systems: Networks of Plausible Inference, Morgan Kaufmann Publishers, Los Altos, CA, 1988.
17. D. Poole, The use of conflicts in searching Bayesian networks, in: Proceedings of the Ninth Conference on Uncertainty in Artificial Intelligence, 1993, pp. 359-367.
18. M. Pradhan, G. Provan, B. Middleton, M. Henrion, Knowledge engineering for large belief networks, in: Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence, 1994. pp. 484-490.
19. G.M. Provan, Abstraction in belief networks: The role of intermediate sates in diagnostic reasoning, in: Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence, 1995, pp. 464-471.
20. G. Shafer, P. Shenoy, Probability propagation, Annals of Mathematics and Artificial Intelligence 2 (1990) 327-352.
21. S. Srinivas, A generalization of the noisy-or model, in: Proceedings of the Ninth Conference on Uncertainty in Artificial Intelligence, 1993, pp. 208-215.
22. M.P. Wellman, C.-L. Liu, State-space abstraction for anytime evaluation of probabilistic networks, in: Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence, 1994, pp. 567-574.
23. N.L. Zhang, D. Poole, Exploiting causal independence in Bayesian network inference, Journal of Artificial Intelligence 5 (1996) 301-328.