Inside-outside probability computation for belief propagation

IJCAI International Joint Conference on Artificial Intelligence

Volumn , Issue , 2007, Pages 2605-2610

  Sato, Taisuke ^a  

^a TOKYO INSTITUTE OF TECHNOLOGY   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

BELIEF PROPAGATION; FORWARD BACKWARD ALGORITHMS; HIDDEN MARKOV MODELS (HMMS); JUNCTION TREES; PROBABILISTIC LOGIC PROGRAMS; PROBABILITY COMPUTATIONS;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; FORESTRY; HIDDEN MARKOV MODELS; LOGIC PROGRAMMING;

BACKPROPAGATION;

EID: 84880862205     PISSN: 10450823     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (13)

1. J. K. Baker. Trainable grammars for speech recognition. In Proceedings of Spring Conference of the Acoustical Society of America, pages 547-550, 1979.
2. M. Chavira and A. Darwiche. Compiling bayesian networks with local structure. In Proceedings of the 19th International Joint Conference on Artificial Intelligence (IJCAI'05), pages 1306-1312, 2005.
3. K. Doets. From Logic to Logic Programming. The MIT Press, 1994.
4. F. V. Jensen. An Introduction to Bayesian Networks. UCL Press, 1996.
5. K. Kask, R. Dechter, J Larrosa, and F. Cozman. Bucket-tree elimination for automated reasoning. ICS Technical Report Technical Report No.R92, UC Irvine, 2001.
6. J.D. Lafferty. A derivation of the Inside-Outside Algorithm from the EM algorithm. Technical Report TR-IT-0056, IBM T.J.Watson Research Center, 1993.
7. S.L. Lauritzen and D.J. Spiegelhalter. Local computations with probabilities on graphical structures and their applications to expert systems. Journal of the Royal Statistical Society, B, 50:157-224, 1988.
8. D. McAllester, M. Collins, and F. Pereira. Case-factor diagrams for structured probabilistic modeling. In Proceedings of the 20th Annual Conference on Uncertainty in Artificial Intelligence (UAI-04), pages 382-391, Arlington, Virginia, 2004. AUAI Press.
9. D. V. Pynadath and M. P. Wellman. Generalized queries on probabilistic context-free grammars. In Proceedings of the 14th National Conference on Artificial Intelligence (AAAI'96), pages 1285-1290, 1996.
10. T. Sato and Y. Kameya. Parameter learning of logic programs for symbolic-statistical modeling. Journal of Artificial Intelligence Research, 15:391-454, 2001.
11. G.R. Shafer and P.P. Shenoy. Probability propagation. Annals of Mathematics and Artificial Intelligence, 2:327-352, 1990.
12. P. Smyth, D. Heckerman, and M. Jordan. Probabilistic independence networks for hidden markov probability models. Neural Computation, 9(2):227-269, 1997. (Pubitemid 127635390)
13. Neng-Fa Zhou and T. Sato. Efficient Fixpoint Computation in Linear Tabling. In Proceedings of the Fifth ACM-SIGPLAN International Conference on Principles and Practice of Declarative Programming (PPDP2003), pages 275-283, 2003.