Tree approximation for belief updating

Proceedings of the National Conference on Artificial Intelligence

Volumn , Issue , 2002, Pages 553-559

  Mateescu, Robert ^a   Dechter, Rina ^a   Kask, Kalev ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; APPROXIMATION THEORY; PROBABILITY; PROBLEM SOLVING;

TREE APPROXIMATION;

TREES (MATHEMATICS);

EID: 0036927581     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (26)

1. Arnborg, S. A. 1985. Efficient algorithms for combinatorial problems on graphs with bounded decomposability - a survey. BIT 25:2-23.
2. Boyen, X., and Koller, D. 1998. Tractable inference in complex stochastic processes. In Artificial Intelligence (UAI'98), 33-42.
3. Cheng, J., and Druzdzel, M. 2000. AIS-BN: An adaptive importance sampling algorithm for evidential reasoning in large bayesian networks. Journal of Artificial Intelligence Research 13:155-188.
4. Cooper, G. 1990. The computational complexity of probabistic inferences. Artificial Intelligence 393-405.
5. Dagum, P., and Luby, M. 1993. Approximating probabilistic inference in bayesian belief networks is np-hard. In National Conference on Artificial Intelligence (AAAI-93).
6. Dechter, R., and Rish, I. 1997. A scheme for approximating probabilistic inference. In Artificial Intelligence (UAI'97), 132-141.
7. Dechter, R.; Kask, K.; and Larossa, J. 2001. A general scheme for multiple lower bound computation in constraint optimization. In Constraint Programming.
8. Dechter, R.; Kask, K.; and Mateescu, R. 2002. Iterative join-graph propagation. Technical report, UCI.
9. Dechter, R. 1996. Bucket elimination: A unifying framework for probabilistic inference algorithms. In Uncertainty in Artificial Intelligence (UAI'96), 211-219.
10. Gottlob, G.; Leone, N.; and Scarello, F. 1999. A comparison of structural CSP decomposition methods. IJCAI-99.
11. Henrion, M. 1986. Propagating uncertainty by logic sampling. In Technical report, Department of Engineering and Public Policy, Carnegie Melon University.
12. Jaakkola, T. S., and Jordan, M. I. 1996. Recursive algorithms for approximating probabilities in graphical models. Advances in Neural Information Processing Systems 9.
13. Jensen, F.; Lauritzen, S.; and Olesen, K. 1990. Bayesian updating in causal probabilistic networks by local computation. Computational Statistics Quarterly 4:269-282.
14. Kask, K. 2001. Approximation algorithms for graphical models. Technical report, Ph.D. thesis, Information and Computer Science, University of California, Irvine.
15. Kjæaerulff, U. 1994. Reduction of computational complexity in bayesian networks through removal of weak dependencies. In Uncertainty in Artificial Intelligence (UAI'94).
16. Koller, D.; Lerner, U.; and Angelov, D. 1998. A general algorithm for approximate inference and its applciation to hybrid bayes nets. In Uncertainty in Artificial Intelligence (UAI'98), 324-333.
17. Lauritzen, S., and Spiegelhalter, D. 1988. Local computation with probabilities on graphical structures and their application to expert systems. Journal of the Royal Statistical Society, Series B 50(2): 157-224.
18. McEliece, R.; MacKay, D.; and Cheng, J.-F. 1997. Turbo decoding as an instance of pearl's belief propagation algorithm. IEEE J. Selected Areas in Communication.
19. Pearl, J. 1988. Probabilistic Reasoning in Intelligent Systems. Morgan Kaufmann.
20. Roth, D. 1996. On the hardness of approximate reasoning. AI 82(1-2):273-302.
21. Shachter, R. D., and Peot, M. A. 1989. Simulation approaches to general probabilistic inference on belief networks. In Uncertainty in Artificial Intelligene (UAI'89).
22. Shafer, G. R., and Shenoy, P. 1990. Probability propagation. Anals of Mathematics and Artificial Intelligence 2:327-352.
23. Weiss, Y. 1997. Belief propagation and revision in networks with loops. In NIPS.
24. Welling, M., and Teh, Y. 2001. Belief optimization for binary networks: A stable alternative to loopy belief propagation. In UAI'01, 554-561.
25. Yedidia, J. S.; Freeman, W.; and Weiss, Y. 2001. Generalized belief propagation. In Advances in Neural Information Processing Systems 13.
26. Zhang, N., and Poole, D. 1994. A simple algorithm for bayesian network computations. In Proc of the tenth Canadian Conference on Artificial Intelligence, 171-178.