Generative modeling by PRISM

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5649 LNCS, Issue , 2009, Pages 24-35

  Sato, Taisuke ^a  

^a TOKYO INSTITUTE OF TECHNOLOGY   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

GENERATIVE MODELING; OBSERVED DATA; PROBABILISTIC EXTENSION; PROBABILISTIC MODELING; PROGRAM SYNTHESIS; STATISTICAL PARAMETERS; TECHNICAL DETAILS;

LOGIC PROGRAMMING; PRISMS; WIRELESS TELECOMMUNICATION SYSTEMS;

PROLOG (PROGRAMMING LANGUAGE);

EID: 69949153889     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-02846-5_4     Document Type: Conference Paper

References (46)

1. Poole, D.: Probabilistic Horn abduction and Bayesian networks. Artificial Intelligence 64(1), 81-129 (1993)
2. Sato, T.: A statistical learning method for logic programs with distribution semantics. In: Proceedings of the 12th International Conference on Logic Programming (ICLP 1995), pp. 715-729 (1995)
3. Muggleton, S.: Stochastic logic programs. In: De Raedt, L. (ed.) Advances in Inductive Logic Programming, pp. 254-264. IOS Press, Amsterdam (1996)
4. Poole, D.: The independent choice logic for modeling multiple agents under uncertainty. Artificial Intelligence 94(1-2), 7-56 (1997)
5. Sato, T., Kameya, Y.: Parameter learning of logic programs for symbolic-statistical modeling. Journal of Artificial Intelligence Research 15, 391-454 (2001)
6. Kersting, K., De Raedt, L.: Basic principles of learning bayesian logic programs. Technical Report Technical Report No. 174, Institute for Computer Science, University of Freiburg (2002)
7. Blockeel, H.: Prolog for Bayesian networks: a meta-interpreter approach. In: Proceedings of the 2nd International Workshop on Multi-Relational Data Mining (MRDM 2003), pp. 1-13 (2003)
8. Vennekens, J., Verbaeten, S., Bruynooghe, M.: Logic programs with annotated disjunctions. In: Demoen, B., Lifschitz, V. (eds.) ICLP 2004. LNCS, vol. 3132, pp. 431-445. Springer, Heidelberg (2004)
9. Fierens, D., Blockeel, H., Bruynooghe, M., Ramon, J.: Logical Bayesian networks and their relation to other probabilistic logical models. In: Kramer, S., Pfahringer, B. (eds.) ILP 2005. LNCS, vol. 3625, pp. 121-135. Springer, Heidelberg (2005)
10. De Raedt, L., Kimmig, A., Toivonen, H.: ProbLog: A probabilistic Prolog and its application in link discoverry. In: Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI 2007), pp. 2468-2473 (2007)
11. Baral, C., Gelfond, M., Rushton, N.: Probabilistic reasoning with answer sets. Theory and Practice of Logic Programming (TPLP) 9(1), 57-144 (2009)
12. De Raedt, L., Kersting, K.: Probabilistic inductive logic programming. In: De Raedt, L., Frasconi, P., Kersting, K., Muggleton, S. (eds.) Probabilistic Inductive Logic Programming - Theory and Applications. LNCS, pp. 1-27. Springer, Heidelberg (2008)
13. Breese, J.S.: Construction of belief and decision networks. Computational Intelligence 8(4), 624-647 (1992)
14. Koller, D., Pfeffer, A.: Learning probabilities for noisy first-order rules. In: Proceedings of the 15th International Joint Conference on Artificial Intelligence (IJCAI 1997), pp. 1316-1321 (1997)
15. Friedman, N., Getoor, L., Koller, D., Pfeffer, A.: Learning probabilistic relational models. In: Proceedings of the 16th International Joint Conference on Artificial Intelligence (IJCAI 1999), pp. 1300-1309 (1999)
16. Pfeffer, A.: IBAL: A probabilistic rational programming language. In: Proceedings of the 17th International Conference on Artificial Intelligence (IJCAI 2001), pp. 733-740 (2001)
17. Jaeger, J.: Complex probabilistic modeling with recursive relational Bayesian networks. Annals of Mathematics and Artificial Intelligence 32(1-4), 179-220 (2001)
18. Getoor, L., Friedman, N., Koller, D., Taskar, B.: Learning Probabilistic Models of Relational Structure. Journal of Machine Learning Research 3, 679-707 (2002)
19. Costa, V., Page, D., Qazi, M., Cussens, J.: CLP(BN): Constraint logic programming for probabilistic knowledge. In: Proceedings of the 19th Conference on Uncertainty in Artificial Intelligence (UAI 2003), pp. 517-524 (2003)
20. Milch, B., Marthi, B., Russell, S., Sontag, D., Ong, D., Kolobov, A.: BLOG: Probabilistic models with unknown objects. In: Proceedings of the 19th International Joint Conference on Artificial Intelligence (IJCAI 2005), pp. 1352-1359 (2005)
21. Laskey, K.: MEBN: A logic for open-world probabilistic reasoning. C4I Center Technical Report C4I06-01, George Mason University Department of Systems Engineering and Operations Research (2006)
22. Richardson, M., Domingos, P.: Markov logic networks. Machine Learning 62, 107-136 (2006)
23. Getoor, L., Grant, J.: PRL: A probabilistic relational language. Journal of Machine Learning 62(1-2), 7-31 (2006)
24. Getoor, L., Taskar, B. (eds.): Introduction to Statistical Relational Learning. MIT Press, Cambridge (2007)
25. Sato, T., Kameya, Y.: Statistical abduction with tabulation. In: Kakas, A., Sadri, F. (eds.) Computational Logic: Logic Programming and Beyond. LNCS (LNAI), vol. 2408, pp. 567-587. Springer, Heidelberg (2002)
26. Zhou, N.F., Sato, T., Shen, Y.D.: Linear tabling strategies and optimization. Theory and Practice of Logic Programming 8(1), 81-109 (2008)
27. Sato, T.: First Order Compiler: A deterministic logic program synthesis algorithm. Journal of Symbolic Computation 8, 605-627 (1989)
28. Fenstad, J.E.: Representation of probabilities defined on first order languages. In: Crossley, J.N. (ed.) Sets, Models and Recursion Theory, pp. 156-172. North-Holland, Amsterdam (1967)
29. Milch, B., Marthi, B., Sontag, D., Russell, S., Ong, D., Kolobov, A.: Approximate Inference for Infinite Contingent Bayesian Networks. In: Proceedings of the 10th International Workshop on Artificial Intelligence and Statistics (AISTATS 2005), pp. 1352-1359 (2005)
30. Domingos, P., Singla, P.: Markov logic in infinite domains. In: De Raedt, L., Dietterich, T., Getoor, L., Kersting, K., Muggleton, S. (eds.) Probabilistic, Logical and Relational Learning - A Further Synthesis. Dagstuhl Seminar Proceedings, vol. 07161 (2008)
31. Stolcke, A.: An efficient probabilistic context-free parsing algorithm that computes prefix probabilities. Computational Linguistics 21(2), 165-201 (1995)
32. Gelfond, M., Lifshcitz, V.: The stable model semantics for logic programming, pp. 1070-1080 (1988)
33. Van Gelder, A., Ross, K., Schlipf, J.: The well-founded semantics for general logic programs. The journal of ACM (JACM) 38(3), 620-650 (1991)
34. Tamaki, H., Sato, T.: OLD resolution with tabulation. In: Shapiro, E. (ed.) ICLP 1986. LNCS, vol. 225, pp. 84-98. Springer, Heidelberg (1986)
35. Sagonas, K., Swift, T., Warren, D.: XSB as an efficient deductive database engine. In: Proceedings of the 1994 ACM SIGMOD International Conference on Management of Data, pp. 442-453 (1994)
36. Ramakrishnan, I., Rao, P., Sagonas, K., Swift, T., Warren, D.: Efficient tabling mechanisms for logic programs. In: Proceedings of the 12th International Conference on Logic Programming (ICLP 1995), pp. 687-711. The MIT Press, Cambridge (1995)
37. Guo, H.F., Gupta, G.: A simple scheme for implementing tabled logic programming systems based on dynamic reordering of alternatives. In: Codognet, P. (ed.) ICLP 2001. LNCS, vol. 2237, pp. 181-196. Springer, Heidelberg (2001)
38. Sagonas, K., Stuckey, J.: Just enough tabling. In: Proceedings of the 6th ACM SIGPLAN international conference on Principles and practice of declarative programming (PPDP 2004), pp. 78-89. ACM, New York (2004)
39. Rabiner, L.R., Juang, B.: Foundations of Speech Recognition. Prentice-Hall, Englewood Cliffs (1993)
40. Baker, J.K.: Trainable grammars for speech recognition. In: Proceedings of Spring Conference of the Acoustical Society of America, pp. 547-550 (1979)
41. Pearl, J.: Probabilistic Reasoning in Intelligent Systems. Morgan Kaufmann, San Francisco (1988)
42. Sato, T.: Inside-Outside probability computation for belief propagation. In: Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI 2007), pp. 2605-2610 (2007)
43. Sato, T., Kameya, Y., Kurihara, K.: Variational bayes via propositionalized probability computation in prism. Annals of Mathematics and Artificial Intelligence (to appear)
44. Cussens, J.: Parameter estimation in stochastic logic programs. Machine Learning 44(3), 245-271 (2001)
45. Sato, T., Kameya, Y.: PRISM: a language for symbolic-statistical modeling. In: Proceedings of the 15th International Joint Conference on Artificial Intelligence (IJCAI 1997), pp. 1330-1335 (1997)
46. Sato, T., Kameya, Y., Zhou, N.F.: Generative modeling with failure in PRISM. In: Proceedings of the 19th International Joint Conference on Artificial Intelligence (IJCAI 2005), pp. 847-852 (2005)