A tractable pseudo-likelihood function for bayes nets applied to relational data

Proceedings of the 11th SIAM International Conference on Data Mining, SDM 2011

Volumn , Issue , 2011, Pages 462-473

  Schulte, Oliver ^a  

^a SIMON FRASER UNIVERSITY   (Canada)

Author keywords

Bayes Nets; Lattice Search; Pseudo Likelihood; Statistical Relational Learning

Indexed keywords

ARTIFICIAL INTELLIGENCE; DATA MINING; SEMANTICS; SPACE DIVISION MULTIPLE ACCESS;

BAYES NET; CONDITIONAL FREQUENCIES; EXPERIMENTAL VALIDATIONS; LATTICE SEARCH; MARKOV LOGIC NETWORKS; PSEUDO-LIKELIHOOD; STATISTICAL RELATIONAL LEARNING; THEORETICAL FOUNDATIONS;

LEARNING SYSTEMS;

EID: 84865207049     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1137/1.9781611972818.40     Document Type: Conference Paper

References (42)

1. F. BACCHUS, Representing and reasoning with probabilistic knowledge: a logical approach to probabilities, MIT Press, Cambridge, MA, USA, 1990.
2. J. BESAG, Statistical analysis of non-lattice data, The Statistician, 24 (1975), pp. 179-195.
3. I. BRATKO, Prolog (3rd ed.): programming for artificial intelligence, Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA, 2001.
4. L. DE RAEDT, Logical and Relational Learning, Cognitive Technologies, Springer, 2008.
5. P. DOMINGOS AND M. RICHARDSON, Markov logic: A unifying framework for statistical relational learning, in Introduction to Statistical Relational Learning [11].
6. D. FIERENS, H. BLOCKEEL, M. BRUYNOOGHE, AND J. RAMON, Logical bayesian networks and their relation to other probabilistic logical models, in ILP, S. Kramer and B. Pfahringer, eds., vol. 3625 of Lecture Notes in Computer Science, Springer, 2005, pp. 121-135.
7. N. FRIEDMAN AND M. GOLDSZMIDT, Learning Bayesian networks with local structure, in NATO ASI on Learning in graphical models, 1998, pp. 421-459.
8. S. GEMAN AND C. GRAFFINE, Markov random field image models and their application to computer vision., in Proceedings of the 1986 International Congress of Mathematicians, A. Gleason, ed., American Mathematical Society, 1987, pp. 1496-1517.
9. L. GETOOR, N. FRIEDMAN, D. KOLLER, A. PFEFFER, AND B. TASKAR, Probabilistic relational models, in Introduction to Statistical Relational Learning [11], ch. 5, pp. 129-173.
10. L. GETOOR AND B. TASKAR, Introduction, in Getoor and Taskar [11], pp. 1-8.
11. L. GETOOR AND B. TASKER, Introduction to statistical relational learning, MIT Press, 2007.
12. J. Y. HALPERN, An analysis of first-order logics of probability, Artificial Intelligence, 46 (1990), pp. 311-350.
13. D. HECKERMAN, D. M. CHICKERING, C. MEEK, R. ROUNTHWAITE, C. KADIE, AND P. KAELBLING, Dependency networks for inference, collaborative filtering, and data visualization, Journal of Machine Learning Research, 1 (2000), pp. 49-75.
14. D. HECKERMAN, D. GEIGER, AND D. CHICKERING, Learning Bayesian networks: The combination of knowledge and statistical data, Machine Learning, 20 (1995), pp. 197-243.
15. W. KENT, A simple guide to five normal forms in relational database theory, Commun. ACM, 26 (1983), pp. 120-125.
16. K. KERSTING AND L. DE RAEDT, Bayesian logic programming: Theory and tool, in Introduction to Statistical Relational Learning [11], ch. 10, pp. 291-318.
17. H. KHOSRAVI, O. SCHULTE, AND B. BINA, Virtual joins with nonexistent links, 19th Conference on Inductive Logic Programming (ILP), 2009. URL = http://www.cs.kuleuven.be/~dtai/ilp-mlg-srl/papers/ILP09-39.pdf.
18. H. KHOSRAVI, O. SCHULTE, T. MAN, X. XU, AND B. BINA, Structure learning for Markov logic networks with many descriptive attributes, in Proceedings of the Twenty-Fourth Conference on Artificial Intelligence (AAAI)., 2010, pp. 487-493.
19. A. C. KLUG, Equivalence of relational algebra and relational calculus query languages having aggregate functions, J. ACM, 29 (1982), pp. 699-717.
20. S. KOK AND P. DOMINGOS, Learning the structure of Markov logic networks, in ICML, L. D. Raedt and S. Wrobel, eds., ACM, 2005, pp. 441-448.
21. -, Learning markov logic network structure via hypergraph lifting, in ICML, A. P. Danyluk, L. Bottou, and M. L. Littman, eds., ACM, 2009, pp. 64-71.
22. D. KOLLER AND A. PFEFFER, Learning probabilities for noisy first-order rules, in IJCAI, 1997, pp. 1316-1323.
23. G. LACERDA, P. SPIRTES, J. RAMSEY, AND P. O. HOYER, Discovering cyclic causal models by independent components analysis, in UAI, D. A. McAllester and P. Myllymäki, eds., AUAI Press, 2008, pp. 366-374.
24. P. LIANG AND M. I. JORDAN, An asymptotic analysis of generative, discriminative, and pseudolikelihood estimators, in ICML '08: Proceedings of the 25th international conference on Machine learning, New York, NY, USA, 2008, ACM, pp. 584-591.
25. L. MIHALKOVA AND R. J. MOONEY, Bottom-up learning of Markov logic network structure, in ICML, ACM, 2007, pp. 625-632.
26. A. W. MOORE AND M. S. LEE, Cached sufficient statistics for efficient machine learning with large datasets, J. Artif. Intell. Res. (JAIR), 8 (1998), pp. 67-91.
27. R. E. NEAPOLITAN, Learning Bayesian Networks, Pearson Education, 2004.
28. J. NEVILLE AND D. JENSEN, Relational dependency networks, Journal of Machine Learning Research, 8 (2007), pp. 653-692.
29. L. NGO AND P. HADDAWY, Answering queries from context-sensitive probabilistic knowledge bases, Theor. Comput. Sci., 171 (1997), pp. 147-177.
30. J. PEARL, Probabilistic Reasoning in Intelligent Systems, Morgan Kaufmann, 1988.
31. M. PIHLAJA, M. GUTMANN, AND A. HYVRINEN, A family of computationally efficient and simple estimators for unnormalized statistical models, in Uncertainty in Artificial Intelligence (UAI), 2010.
32. D. POOLE, First-order probabilistic inference, in IJCAI, G. Gottlob and T. Walsh, eds., Morgan Kaufmann, 2003, pp. 985-991.
33. R. RAINA, Y. SHEN, A. Y. NG, AND A. MCCALLUM, Classification with hybrid generative/discriminative models, in NIPS, S. Thrun, L. K. Saul, and B. Schölkopf, eds., MIT Press, 2003.
34. T. RICHARDSON, A discovery algorithm for directed cyclic graphs, in UAI, E. Horvitz and F. V. Jensen, eds., Morgan Kaufmann, 1996, pp. 454-461.
35. P. SPIRTES, C. GLYMOUR, AND R. SCHEINES, Causation, Prediction, and Search, MIT Press, 2000.
36. B. TASKAR, P. ABBEEL, AND D. KOLLER, Discriminative probabilistic models for relational data, in UAI, A. Darwiche and N. Friedman, eds., Morgan Kaufmann, 2002, pp. 485-492.
37. J. D. ULLMAN, Principles of database systems, 2, Computer Science Press, 1982.
38. M. Y. VARDI, On the complexity of bounded-variable queries, in PODS, ACM Press, 1995, pp. 266-276.
39. M. WELLMAN, J. BREESE, AND R. GOLDMAN, From knowledge bases to decision models, Knowledge Engineering Review, 7 (1992), p. 35-53.
40. R. XIANG AND J. NEVILLE, Pseudolikelihood em for within-network relational learning, in ICDM, IEEE Computer Society, 2008, pp. 1103-1108.
41. Z. XU, K. KERSTING, AND V. TRESP, Multirelational learning with gaussian processes, in IJCAI, C. Boutilier, ed., 2009, pp. 1309-1314.
42. X. YIN, J. HAN, J. YANG, AND P. S. YU, Crossmine: Efficient classification across multiple database relations, in Constraint-Based Mining and Inductive Databases, 2004, pp. 172-195.