Compiling relational Bayesian networks for exact inference

International Journal of Approximate Reasoning

Volumn 42, Issue 1-2, 2006, Pages 4-20

  Chavira, Mark ^a   Darwiche, Adnan ^a   Jaeger, Manfred ^b  

^a Engineering IV   (United States)

^b AALBORG UNIVERSITY   (Denmark)

Author keywords

Bayesian networks; Exact inference; Relational models

Indexed keywords

DIFFERENTIATION (CALCULUS); INFERENCE ENGINES;

ARITHMETIC CIRCUITS; BAYESIAN NETWORKS; EXACT INFERENCE; RELATIONAL MODELS;

STATISTICAL METHODS;

EID: 33645958620     PISSN: 0888613X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ijar.2005.10.001     Document Type: Article

References (29)

1. Jensen F.V., Lauritzen S., and Olesen K. Bayesian updating in recursive graphical models by local computation. Computational Statistics Quarterly 4 (1990) 269-282
2. Darwiche A. A differential approach to inference in Bayesian networks. Journal of the ACM 50 3 (2003) 280-305
3. F. Jensen, S.K. Andersen, Approximations in Bayesian belief universes for knowledge based systems, in: Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI), Cambridge, MA, 1990, pp. 162-169.
4. Park J., and Darwiche A. A differential semantics for jointree algorithms. Artificial Intelligence 156 (2004) 197-216
5. Breese J.S., Goldman R.P., and Wellman M.P. Introduction to the special section on knowledge-based construction of probabilistic decision models. IEEE Transactions on Systems, Man, and Cybernetics 24 11 (1994) 1577-1579
6. T. Sato, A statistical learning method for logic programs with distribution semantics, in: Proceedings of the International Conference on Logic Programming (ICLP), 1995, pp. 715-729.
7. Muggleton S. Stochastic logic programs. In: de Raedt L. (Ed). Advances in Inductive Logic Programming (1996), IOS Press 254-264
8. K. Kersting, L. de Raedt, Towards combining inductive logic programming and Bayesian networks, in: Proceedings of the International Conference on Inductive Logic Programming (ILP), Springer Lecture Notes in AI 2157, 2001.
9. K.B. Laskey, S.M. Mahoney, Network fragments: representing knowledge for constructing probabilistic models, in: Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI), San Francisco, CA, 1997, pp. 334-341.
10. D. Koller, A. Pfeffer, Probabilistic frame-based systems, in: Proceedings of the National Conference on Artificial Intelligence (AAAI), 1998, pp. 580-587.
11. N. Friedman, L. Getoor, D. Koller, A. Pfeffer, Learning probabilistic relational models, in: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), 1999.
12. M. Jaeger, Relational Bayesian networks, in: D. Geiger, P.P. Shenoy (Eds.), Proceedings of the Conference of Uncertainty in Artificial Intelligence (UAI), Providence, USA, 1997, pp. 266-273.
13. H. Pasula, S. Russell, Approximate inference for first-order probabilistic languages, in: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), 2001, pp. 741-748.
14. M. Richardson, P. Domingos, Markov logic networks, Special issue of the Machine Learning Journal on Statistical Relational Learning and Multi-Relational Data Mining, in press.
15. Jaeger M. Complex probabilistic modeling with recursive relational Bayesian networks. Annals of Mathematics and Artificial Intelligence 32 (2001) 179-220
16. Jaeger M. On the complexity of inference about probabilistic relational models. Artificial Intelligence 117 (2000) 297-308
17. D. Poole, First-order probabilistic inference, in: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), 2003.
18. R. de Salvo Braz, E. Amir, D. Roth, Lifted first-order probabilistic inference, in: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), 2005, pp. 1319-1325.
19. A. Darwiche, A logical approach to factoring belief networks, in: Proceedings of the International Conference on Principles of Knowledge Representation and Reasoning (KR), 2002, pp. 409-420.
20. Sang T., Beame P., and Kautz H. Solving Bayesian networks by weighted model counting. Proceedings of the National Conference on Artificial Intelligence (AAAI) vol. 1 (2005), AAAI Press 475-482
21. J. Huang, A. Darwiche, DPLL with a trace: from sat to knowledge compilation, in: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), 2005, pp. 156-162.
22. Darwiche A., and Marquis P. A knowledge compilation map. Journal of Artificial Intelligence Research 17 (2002) 229-264
23. Darwiche A., and Marquis P. Compiling propositional weighted bases. Artificial Intelligence 157 1-2 (2004) 81-113
24. A. Darwiche, New advances in compiling CNF to decomposable negational normal form, in: Proceedings of the European Conference on Artificial Intelligence (ECAI), 2004, pp. 328-332.
25. M. Chavira, A. Darwiche, Compiling Bayesian networks with local structure, in: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), 2005, pp. 1306-1312.
26. C. Boutilier, N. Friedman, M. Goldszmidt, D. Koller, Context-specific independence in Bayesian networks, in: Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI), 1996, pp. 115-123.
27. Shachter R.D. Evaluating influence diagrams. Operations Research 34 6 (1986) 871-882
28. Ross S. Evidence absorption and propagation through evidence reversals. Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI) (1990), Elsevier Science Publishing Company, Inc., New York, NY
29. M. Chavira, D. Allen, A. Darwiche, Exploiting evidence in probabilistic inference, in: Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI), 2005, pp. 112-119.