Block Learning Bayesian Network Structures from Data

Yi-Feng Zeng

Abstract

We present a block learning algorithm for learning large Bayesian network structures from limited data in a distributed fashion. Adopting divide-and-conquer strategy, firstly, the block learning algorithm discovers local structures of Bayesian networks individually with preferred learning techniques. After that, the learned local structures are combined together to recover the final Bayesian network. A serial of experiments on golden networks demonstrate that the block learning algorithm is able to learn a large Bayesian network structure from a small data set. Moreover, this algorithm is scalable and is capable to encompass the existing learning techniques. A study on the learning granularity, the learning engine and the algorithm design discovers that the block learning algorithm provides a uniform view on various learning techniques. Consequently, a unifying learning framework is built on the basis of the block learning algorithm.

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