A statistical method for structure learning of Bayesian networks from data

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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.
Publisher
ACM
Issue Date
2009-08-27
Language
English
Citation

International Conference on Convergence and Hybrid Information Technology 2009, ICHIT 2009, pp.75 - 78

DOI
10.1145/1644993.1645007
URI
http://hdl.handle.net/10203/154416
Appears in Collection
MA-Conference Papers(학술회의논문)
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