Bayesian Model Averaging Using the k-best Bayesian Network Structures
This work addresses the challenge of structural discovery in Bayesian networks for researchers and practitioners in machine learning and statistics, offering an incremental improvement over existing methods.
The paper tackles the problem of learning Bayesian network structures from data by developing an algorithm to find the k-best structures and using Bayesian model averaging over them to compute posterior probabilities, showing that this method outperforms model selection and state-of-the-art MCMC methods in empirical tests on real and synthetic datasets.
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 state of-the-art MCMC methods.