Bayesian model averaging using the κ-best Bayesian network structures

Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, UAI 2010

Volumn , Issue , 2010, Pages 589-597

  Tian, Jin ^a   He, Ru ^a   Ram, Lavanya ^a  

^a Iowa State University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE;

BAYESIAN MODEL AVERAGING; BAYESIAN NETWORK STRUCTURE; LEARNING BAYESIAN NETWORKS; MCMC METHOD; MODEL SELECTION METHODS; POSTERIOR PROBABILITY; STATE OF THE ART; SYNTHETIC DATASETS;

BAYESIAN NETWORKS;

EID: 80053135734     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (16)

1. A. Asuncion and D.J. Newman. UCI machine learning repository, 2007.
2. G. F. Cooper and E. Herskovits. A Bayesian method for the induction of probabilistic networks from data. Machine Learning, 9:309-347, 1992.
3. D. Eaton and K. Murphy. Bayesian structure learning using dynamic programming and MCMC. In Proc. of Conference on Uncertainty in Artificial Intelligence, 2007.
4. Nir Friedman, Moises Goldszmidt, and Abraham Wyner. Data analysis with bayesian networks: A bootstrap approach. In Proceedings of the Proceedings of the Fifteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-99), pages 196-20, San Francisco, CA, 1999. Morgan Kaufmann.
5. Nir Friedman and Daphne Koller. Being bayesian about network structure: A bayesian approach to structure discovery in bayesian networks. Machine Learning, 50(1-2):95-125, 2003.
6. D. Heckerman, D. Geiger, and D.M. Chickering. Learning Bayesian networks: The combination of knowledge and statistical data. Machine Learning, 20:197-243, 1995.
7. D. Heckerman, C. Meek, and G. Cooper. A Bayesian approach to causal discovery. In Glymour C. and Cooper G.F., editors, Computation, Causation, and Discovery, Menlo Park, CA, 1999. AAAI Press and MIT Press.
8. M. Koivisto. Advances in exact Bayesian structure discovery in Bayesian networks. In Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI), 2006.
9. M. Koivisto and K. Sood. Exact Bayesian structure discovery in Bayesian networks. Journal of Machine Learning Research, 5:549-573, 2004.
10. D. Madigan and A. Raftery. Model selection and accounting for model uncertainty in graphical models using occam's window. Journal of Americal Statistical Association, 89:1535-1546, 1994.
11. D. Madigan and J. York. Bayesian graphical models for discrete data. International Statistical Review, 63:215-232, 1995.
12. J. Pearl. Causality: Models, Reasoning, and Inference. Cambridge University Press, NY, 2000.
13. T. Silander and P. Myllymaki. A simple approach for finding the globally optimal Bayesian network structure. In Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI), 2006.
14. Ajit P. Singh and AndrewW. Moore. Finding optimal Bayesian networks by dynamic programming. Technical report, Carnegie Mellon University, School of Computer Science, 2005.
15. P. Spirtes, C. Glymour, and R. Scheines. Causation, Prediction, and Search (2nd Edition). MIT Press, Cambridge, MA, 2001.
16. J. Tian and R. He. Computing posterior probabilities of structural features in Bayesian networks. In Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI), 2009.