Bagged structure learning of Bayesian networks

We present a novel approach for density estimation using Bayesian networks when faced with scarce and partially observed data. Our approach relies on Efron's bootstrap frame-work, and replaces the standard model selection score by a bootstrap aggregation objective aimed at sifting out bad decisions during the learning procedure. Unlike previous bootstrap or MCMC based approaches that are only aimed at recovering specific structural features, we learn a concrete density model that can be used for probabilistic generalization. To make use of our objective when some of the data is missing, we propose a bagged structural EM procedure that does not incur the heavy computational cost typically associated with a bootstrap-based approach. We compare our bagged objective to the Bayesian score and the Bayesian information criterion (BIC), as well as other bootstrap-based model selection objectives, and demonstrate its effectiveness in improving generalization performance for varied real-life datasets.