Bayesian Model Averaging Using the k-best Bayesian Network Structures

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.

Authors

• Jin Tian 0001
• Ru He
• Lavanya Ram

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