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A statistical method for structure learning of Bayesian networks from data

Published: 27 August 2009 Publication History

Abstract

The Bayesian network, a powerful tool for predicting and diagnosing uncertain phenomena, is used in various fields including artificial intelligence, business administration, and medical science. We use a statistical approach, and present a simple algorithm for learning Bayesian network structure from data. First we obtain from data the original correlation graph and the correlation graphs when one or two variables are fixed. Then we construct a Bayesian network that would produce the most similar correlation graphs. Simulation results are given to demonstrate that the algorithm determines the network structure with a high accuracy.

References

[1]
Neopolitan, R. E. 2004. Learning Bayesian Networks. Prentice Hall, Chicago, Illinois.
[2]
Friedman, N. and Yakhini, Z. 1996. On the sample complexity of learning Bayesian networks. In Proceedings of the 12th conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann, 274--282.
[3]
Wallace, C. S., and Korb, K. 1999. Learning Linear Causal Models by MML Sampling. In Gammerman, A. (Ed.): Causal Models and Intelligent Data Mining. Springer-Verlag, New York.
[4]
Larranaga, P., Poza, M., Yurramendi, Y., Murga, R. H., and Kuijpers, C. M. H. 1996. Structure learning of Bayesian networks by genetic algorithms: a performance analysis of control parameters. IEEE Transactions on Pattern Analysis and Machine Intelligence, 18, 9 (1996), 912--926.
[5]
de Campos, L. M., Fernandez-Luna, J. M., Gamez, J. A., and Puerta, J. M. 2002. Ant colony optimization for learning Bayesian networks. International Journal of Approximate Reasoning, 31, 3 (2002), 291--311.
[6]
Rebane, G. and Pearl, J. 1987. The recovery of causal polytrees from statistical data. In Proceedings of the Third Conference on Uncertainty in Artificial Intelligence (Seattle, Washington, July 1987), 222--228.

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cover image ACM Other conferences
ICHIT '09: Proceedings of the 2009 International Conference on Hybrid Information Technology
August 2009
687 pages
ISBN:9781605586625
DOI:10.1145/1644993
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 August 2009

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Author Tags

  1. Bayesian network
  2. statistical method
  3. structure learning

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