A general method for reducing the complexity of relational inference and its application to MCMC

Proceedings of the National Conference on Artificial Intelligence

Volumn 2, Issue , 2008, Pages 1075-1080

  Poon, Hoifung ^a   Domingos, Pedro ^a   Sumner, Marc ^a  

^a University of Washington   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

APPLICATIONS; ARTIFICIAL INTELLIGENCE; ATOMIC PHYSICS; ATOMS; BIONICS; FORMAL LOGIC; PROBABILITY;

DEFAULT VALUES; DO-MAINS; EXPONENTIAL TIMES; FIRST ORDERS; GENERAL METHODS; INFERENCE ALGORITHMS; MCMC ALGORITHMS; MEMORY COSTS; ORDERS OF MAGNITUDES; PROBABILISTIC INFERENCES; PROPOSITIONALIZATION; RELATIONAL STRUCTURES; SAT SOLVERS; SPACE AND TIMES;

INFERENCE ENGINES;

EID: 57749204548     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (16)

1. Bakir, G.; Hofmann, T.; Schölkopf, B.; Smola, A.; Taskar, B. and Vishwanathan, S. (eds.) 2007. Predicting Structured Data. MIT Press.
2. Braz, R.; Amir, E. and Roth, D. 2006. MPE and partial inversion in lifted probabilistic variable elimination. In Proc. AAAI-06.
3. Damien, P.; Wakefield, J. and Walker, S. 1999. Gibbs sampling for Bayesian non-conjugate and hierarchical models by auxiliary variables. Journal of the Royal Statistical Society B 61(2).
4. Davis, M.; Logemann, G. and Loveland, D. 1962. A machine program for theorem proving. Communications of the ACM 5(7).
5. Genesereth, M. and Nilsson, N. 1987. Logical Foundations of Artificial Intelligence. Morgan Kaufmann.
6. Getoor, L.; Friedman, N.; Koller, D. and Taskar, B. 2001. Learning probabilistic models of relational structure. In Proc. ICML-01.
7. Getoor, L. & Taskar, B., eds. 2007. Introduction to Statistical Relational Learning. MIT Press.
8. Kautz, H.; Selman, B.; and Jiang, Y. 1997. A general stochastic approach to solving problems with hard and soft constraints. In The Satisfiability Problem: Theory and Applications. AMS.
9. Kok, S.; Singla, P.; Richardson, M.; Domingos, P.; Sumner, M. and Poon, H. 2007. The Alchemy system for statistical relational AI. http://alchemy.cs. washington.edu/.
10. Milch, B. and Russell, S. 2006. General-purpose MCMC inference over relational structures. In Proc. UAI-06.
11. Poon, H. and Domingos, P. 2006. Sound and efficient inference with probabilistic and deterministic dependencies. In Proc. AAAI-06.
12. Poon, H. and Domingos, P. 2007. Joint inference in information extraction. In Proc. AAAI-07.
13. Richardson, M. and Domingos, P. 2006. Markov logic networks. Machine Learning 62(1-2).
14. Selman, B.; Kautz, H.; and Cohen, B. 1996. Local search strategies for satisfiability testing. In Cliques, Coloring, and Satisfiability: Second DIMACS Implementation Challenge. AMS.
15. Singla, P. and Domingos, P. 2006. Memory-efficient inference in relational domains. In Proc. AAAI-06.
16. Wei, W.; Erenrich, J. and Selman, B. 2004. Towards efficient sampling: Exploiting random walk strategies. In Proc. AAAI-04.