An improved admissible heuristic for learning optimal bayesian networks

Uncertainty in Artificial Intelligence - Proceedings of the 28th Conference, UAI 2012

Volumn , Issue , 2012, Pages 924-933

  Yuan, Changhe ^a,c   Malone, Brandon ^b,c  

^a CITY UNIVERSITY OF NEW YORK   (United States)

^b HELSINKI INSTITUTE FOR INFORMATION TECHNOLOGY   (Finland)

^c MISSISSIPPI STATE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BREADTH-FIRST; LEARNING BAYESIAN NETWORKS; LEARNING PROBLEM; SCORING FUNCTIONS; SEARCH ALGORITHMS; SPARSE REPRESENTATION;

ARTIFICIAL INTELLIGENCE; HEURISTIC METHODS; LINEAR PROGRAMMING; OPTIMIZATION;

BAYESIAN NETWORKS;

EID: 84886050172     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (22)

1. Culberson, J. C., and Schaeffer, J. 1998. Pattern databases. Computational Intelligence 14(3):318-334.
2. Cussens, J. 2011. Bayesian network learning with cutting planes. In Proceedings of the Proceedings of the Twenty-Seventh Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-11), 153-160.
3. Corvallis, Oregon: AUAI Press. de Campos, C. P., and Ji, Q. 2010. Properties of bayesian dirichlet scores to learn bayesian network structures. In Twenty-Fourth AAAI Conference on Aritificial Intelligence (AAAI-10), 431-436.
4. de Campos, C. P., and Ji, Q. 2011. Efficient structure learning of bayesian networks using constraints. 12:663-689.
5. Felner, A.; Korf, R. E.; and Hanan, S. 2004. Additive pattern database heuristics. Journal of Artificial Intelligence Research (JAIR) 22:279-318.
6. Frank, A., and Asuncion, A. 2010. UCI machine learning repository
7. Garey, M. R., and Johnson, D. S. 1979. Computers and Intractability: A Guide to the Theory of NP-Completeness. W. H.
8. Freeman. Heckerman, D. 1998. A tutorial on learning with Bayesian networks. In Holmes, D., and Jain, L., eds., Innovations in Bayesian Networks, volume 156 of Studies in Computational Intelligence. Springer Berlin / Heidelberg. 33-82.
9. Jaakkola, T.; Sontag, D.; Globerson, A.; and Meila, M. 2010. Learning Bayesian network structure using LP relaxations. In Proceedings of the 13th InternationalConference on Artificial Intelligence and Statistics (AISTATS).
10. Koivisto, M., and Sood, K. 2004. Exact Bayesian structure discovery in Bayesian networks. Journal of Machine Learning Research 549-573
11. Improving the scalability of optimal Bayesian network learningwith external-memory frontier breadth-first branch and bound search. In Proceedings of the Proceedings of the Twenty-Seventh Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-11), 479-488. Corvallis, Oregon: AUAI Press.
12. Malone, B.; Yuan, C.; and Hansen, E. A. 2011. Memory-efficient dynamic programming for learning optimal Bayesian networks. In Proceedings of the 25th AAAI Conference on Artificial Intelligence (AAAI-11), 1057-1062.
13. Ott, S.; Imoto, S.; and Miyano, S. 2004. Finding optimal models for small gene networks. In Pac. Symp. Biocomput, 557-567.
14. Papadimitriou, C. H., and Steiglitz, K. 1982.Combinatorial Optimization: Algorithms and Complexity. Prentice-Hall.
15. Parviainen, P., and Koivisto, M. 2009. Exact structure discovery in Bayesian networks with less space. In Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence. Montreal, Quebec, Canada: AUAI Press.
16. Pearl, J. 1984. Heuristics: Intelligent Search Strategies for Computer Problem Solving. Addison &Wesley
17. Rissanen, J. 1978. Modeling by shortest data description. Automatica 14:465-471.
18. Silander, T., and Myllymaki, P. 2006. A simple approach for finding the globally optimal Bayesian network structure. In Proceedings of the 22nd Annual Conference on Uncertainty in Artificial Intelligence (UAI-06). Arlington, Virginia: AUAI Press.
19. Singh, A., and Moore, A. 2005. Finding optimal Bayesian networks by dynamic programming. Technical report, Carnegie Mellon University.
20. Teyssier, M., and Koller, D. 2005. Ordering-based search: A simple and effective algorithmfor learning Bayesian networks. In Proceedings of the Twenty-First Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-05), 584-590. Arlington, Virginia: AUAI Press.
21. Tian, J. 2000. A branch-and-bound algorithm for MDL learning Bayesian networks. In Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence, 580-588. Morgan Kaufmann Publishers Inc.
22. Yuan, C.; Malone, B.; and Wu, X. 2011. Learning optimal Bayesian networks using A search. In Proceedings of the 22nd International Joint Conference on Artificial Intelligence (IJCAI-11), 2186-2191.