Finding consensus bayesian network structures

Journal of Artificial Intelligence Research

Volumn 42, Issue , 2011, Pages 661-687

  Pẽna, Jose M ^a  

^a LINKÖPING UNIVERSITY   (Sweden)

Author keywords

[No Author keywords available]

Indexed keywords

BAYESIAN NETWORK STRUCTURE; NODE ORDERING; NP-HARD;

BAYESIAN NETWORKS; HEURISTIC ALGORITHMS; LEARNING ALGORITHMS;

HEURISTIC METHODS;

EID: 84856507856     PISSN: None     EISSN: 10769757     Source Type: Journal    
DOI: 10.1613/jair.3427     Document Type: Article

References (28)

1. Chickering, D. M. A Transformational Characterization of Equivalent Bayesian Network Structures. In Proceedings of the Eleventh Conference on Uncertainty in Artificial Intel-ligence, 87-98, 1995.
2. Chickering, D. M. Optimal Structure Identification with Greedy Search. Journal of Machine Learning Research, 3:507-554, 2002.
3. Chickering, D. M. & Meek, C. Finding Optimal Bayesian Networks. In Proceedings of the Eighteenth Conference on Uncertainty in Artificial Intelligence, 94-102, 2002.
4. Chickering, D. M., Heckerman, D. & Meek, C. Large-Sample Learning of Bayesian Networks is NP-Hard. Journal of Machine Learning Research, 5:1287-1330, 2004.
5. Friedman, N. & Koller, D. Being Bayesian About Network Structure. A Bayesian Approach to Structure Discovery in Bayesian Networks. Machine Learning, 50:95-12, 2003.
6. Gavril, F. Some NP-Complete Problems on Graphs. In Proceedings of the Eleventh Confer-ence on Information Sciences and Systems, 91-95, 1977.
7. Garey, M. & Johnson, D. Computers and Intractability: A Guide to the Theory of NP-Completeness. W. H. Freeman, 1979.
8. Geiger, D., Verma, T. & Pearl, J. Identifying Independence in Bayesian Networks. Networks, 20:507-534, 1990.
9. Genest, C. & Zidek, J. V. Combining Probability Distributions: A Critique and an Anno-tated Bibliography. Statistical Science, 1:114-148, 1986.
10. Hartemink, A. J., Gifford, D. K., Jaakkola, T. S. & Young, R. A. Combining Location and Expression Data for Principled Discovery of Genetic Regulatory Network Models. In Pacific Symposium on Biocomputing 7, 437-449, 2002.
11. Jackson, B. N., Aluru, S. & Schnable, P. S. Consensus Genetic Maps: A Graph Theo-retic Approach. In Proceedings of the 2005 IEEE Computational Systems Bioinformatics Conference, 35-43, 2005.
12. Koller, D. & Friedman, N. Probabilistic Graphical Models: Principles and Techniques. MIT Press, 2009.
13. Matzkevich, I. & Abramson, B. The Topological Fusion of Bayes Nets. In Proceedings of the Eight Conference Conference on Uncertainty in Artificial Intelligence, 191-198, 1992.
14. Matzkevich, I. & Abramson, B. Some Complexity Considerations in the Combination of Belief Networks. In Proceedings of the Ninth Conference Conference on Uncertainty in Artificial Intelligence, 152-158, 1993a.
15. Matzkevich, I. & Abramson, B. Deriving a Minimal I-Map of a Belief Network Relative to a Target Ordering of its Nodes. In Proceedings of the Ninth Conference Conference on Uncertainty in Artificial Intelligence, 159-165, 1993b.
16. Maynard-Reid II, P. & Chajewska, U. Agregating Learned Probabilistic Beliefs. In Pro-ceedings of the Seventeenth Conference in Uncertainty in Artificial Intelligence, 354-361, 2001.
17. Meek, C. Graphical Models: Selecting Causal and Statistical Models. Ph D thesis, Carnegie Mellon Unversity, 1997.
18. Ng, K.-C. & Abramson, B. Probabilistic Multi-Knowledge-Base Systems. Journal of Applied Intelligence, 4:219-236, 1994.
19. Nielsen, J. D., Kocka, T. & Peña, J. M. On Local Optima in Learning Bayesian Networks. In Proceedings of the Nineteenth Conference on Uncertainty in Artificial Intelligence, 435-442, 2003.
20. Nielsen, S. H. & Parsons, S. An Application of Formal Argumentation: Fusing Bayesian Networks in Multi-Agent Systems. Artificial Intelligence 171:754-775, 2007. (Pubitemid 47285075)
21. Pearl, J. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, 1988.
22. Pennock, D. M. & Wellman, M. P. Graphical Representations of Consensus Belief. In Pro-ceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence, 531-540, 1999.
23. Peña, J. M., Nilsson, R., Bj̈orkegren, J. & Tegńer, J. Towards Scalable and Data Efficient Learning of Markov Boundaries. International Journal of Approximate Reasoning, 45:211-232, 2007. (Pubitemid 46880195)
24. Peña, J. M., Ko?cka, T. & Nielsen, J. D. Featuring Multiple Local Optima to Assist the User in the Interpretation of Induced Bayesian Network Models. In Proceedings of the Tenth International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, 1683-1690, 2004.
25. Richardson, M. & Domingos, P. Learning with Knowledge from Multiple Experts. In Pro-ceedings of the Twentieth International Conference on Machine Learning, 624-631, 2003.
26. del Sagrado, J. & Moral, S. Qualitative Combination of Bayesian Networks. International Journal of Intelligent Systems, 18:237-249, 2003.
27. Studený, M. Bayesian Networks from the Point of View of Chain Graphs. In Proceedings of the Fourteenth Conference Conference on Uncertainty in Artificial Intelligence, 496-503, 1998.
28. Studený, M. & Bouckaert, R. R. On Chain Graph Models for Description of Conditional Independence Structures. The Annals of Statistics, 26:1434-1495, 1998.