Non-minimal triangulations for mixed stochastic/deterministic graphical models

Proceedings of the 22nd Conference on Uncertainty in Artificial Intelligence, UAI 2006

Volumn , Issue , 2006, Pages 15-22

  Bartels, Chris D ^a   Bilmes, Jeff A ^a  

^a University of Washington   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATIONAL REQUIREMENTS; CORRECTNESS PROOFS; DECISION PROBLEMS; EMPIRICAL RESULTS; GRAPHICAL MODEL; NP COMPLETE; OPTIMAL STATE; OPTIMAL TRIANGULATION; REAL-WORLD GRAPHS; STATE SPACE;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; GRAPHIC METHODS; OPTIMIZATION;

TRIANGULATION;

EID: 80053192471     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (34)

1. S. M. Aji and J. McEliece. The generalized distributive law. IEEE Trans. on Information Theory, 46:325-343, 2000.
2. S. Arnborg, D. J. Corneil, and A. P. Proskurowski. Complexity of finding embeddings in a k-tree. SIAM J. Alg. Dis. Meth., 8:227-284, 1987.
3. F. Bacchus, S. Dalmao, and T. Pitassi. Value elimination: Bayesian inference via backtracking search. In Proc. UAI, 2003.
4. J. Bilmes and C. Bartels. On triangulating dynamic graphical models. In Proc. UAI, Acapulco, Mexico, 2003.
5. J. Bilmes, G. Zweig, and et. al. Discriminatively structured dynamic graphical models for speech recognition. In In Final Report: JHU 2001 Summer Workshop, 2001.
6. H. L. Bodlaender, A. M. Koster, F. van den Eijkhof, and L. C. van der Gaag. Pre-processing for triangulation of probabilistic networks. In Proc. UAI, 2001.
7. A. Darwiche. Recursive conditioning. Artificial Intelligence, 126:5-41, 2001. (Pubitemid 32196230)
8. M. Davis and H. Putnam. A computing procedure for quantification theory. J. ACM, 7(3):201-215, 1960.
9. R. Dechter. Bucket elimination: A unifying framework for several probabilistic inference algorithms. In Proc. UAI, 1996.
10. R. Dechter. Constraint Processing. Morgan Kaufmann, 2003.
11. R. Dechter and Y. E. Fattah. Topological parameters for time-space tradeoff. Artificial Intelligence, 125(1):93-118, January 2001. (Pubitemid 32130120)
12. R. Dechter and D. Larkin. Hybrid processing of beliefs and constraints. In Proc. UAI, 2001.
13. R. Dechter and R. Mateescu. Mixtures of deterministic-probabilistic networks and their and/or search space. In Proc. UAI, 2004.
14. K. Filali and J. Bilmes. A dynamic Bayesian framework to model context and memory in edit distance learning: An application to pronunciation classification. In Proc. ACL, June 2005.
15. M. Fishelson and D. Geiger. Optimizing exact genetic linkage computations. Journal of Computational Biology, 11(2-3):263-75, 2004. (Pubitemid 38901663)
16. F. Jensen and S. Andersen. Approximations in bayesian belief universes for knowledge-based systems. In Proc. UAI, 1990.
17. F. Jensen and F. Jensen. Optimal junction trees. In Proc. UAI, 1994.
18. M. Jordan and C. M. Bishop, editors. An Introduction to Graphical Models. to be published, 200x.
19. U. Kjaerulff. Triangulation of graphs-algorithms giving small total state space. Technical Report R-90-09, Aalborg University, March 1990.
20. D. Larkin and R. Dechter. Bayesian inference in the presence of determinism. In Proc. AISTATS, 2003.
21. S. Lauritzen. Graphical Models. Oxford Science Publications, 1996.
22. S. Lauritzen and D. Spiegelhalter. Local computations with probabilities on graphical structures and their application to expert systems. The Journal of the Royal Statistical Society, 50:57-224, 1988.
23. Y. Mao, F. Kschischang, and B. Frey. Convolutional factor graphs as probabilistic models. In Proc. UAI, 2004.
24. M. Meila and M. I. Jordan. Triangulation by continuous embedding. In Advances in Neural Information Processing Systems, volume 9, 1997.
25. T. Ohtsuki, L. K. Cheung, and T. Fujisawa. Minimal trian-gulation of a graph and optimal pivoting order in a sparse matrix. J. Math. Anal. Appl., 54:622-633, 1976.
26. A. Parra and P. Scheffler. How to use the minimal separators of a graph for its chordal triangulation. In Automata, Languages and Programming, pages 123-134, 1995.
27. J. Pearl. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, 2nd printing edition, 1988.
28. D. J. Rose. Triangulated graphs and the elimination process. J. Math. Anal. Appl., 32:597-609, 1970.
29. D. J. Rose, R. E. Tarjan, and G. S. Lueker. Algorithmic aspects of vertex elimination on graphs. SIAM J. Comp., 5:266-283, 1976.
30. R. E. Tarjan and M. Yannakakis. Simple linear-time algorithms to test chordality of graphs, test acyclicity of hypergraphs, and selectively reduce acyclic hypergraphs. SIAM J. Comp., 13:566-579, 1984. (Pubitemid 15514404)
31. J. Vomlel. Exploiting functional dependence in bayesian network inference. In Proc. UAI, 2002.
32. W. Wilson. Optimal decomposition of belief networks. In Proc. UAI, 1991.
33. L. Xin, G. Ji, J. Bilmes, and M. Ostendorf. DBN multi-stream models for Mandarin toneme recognition. In Proc. ICASSP, 2005.
34. Y. Zhang, Q. Diao, S. Huang, W. Hu, C. Bartels, and J. Bilmes. DBN based multi-stream models for speech. In Proc. ICASSP, Hong Kong, China, April 2003.