Uncertainty in Artificial Intelligence
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Bayesian Model Averaging Using the k-best Bayesian Network Structures
Jin Tian, Ru He, Lavanya Ram
We study the problem of learning Bayesian network structures from data. We develop an algorithm for finding the k-best Bayesian network structures. We propose to compute the posterior probabilities of hypotheses of interest by Bayesian model averaging over the k-best Bayesian networks. We present empirical results on structural discovery over several real and synthetic data sets and show that the method outperforms the model selection method and the stateof- the-art MCMC methods.
Pages: 589-597
PS Link:
PDF Link: /papers/10/p589-tian.pdf
AUTHOR = "Jin Tian and Ru He and Lavanya Ram",
TITLE = "Bayesian Model Averaging Using the k-best Bayesian Network Structures",
BOOKTITLE = "Proceedings of the Twenty-Sixth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-10)",
ADDRESS = "Corvallis, Oregon",
YEAR = "2010",
PAGES = "589--597"

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