A multi-parent search operator for bayesian network building

Authors:
David Iclănzan
Affiliations:
Department of Computer Science, Babe s-Bolyai University, Cluj-Napoca, Romania
Venue:
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I
Year:
2012

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Abstract

Learning a Bayesian network structure from data is a well-motivated but computationally hard task, especially for problems exhibiting synergic multivariate interactions. In this paper, a novel search method for structure learning of a Bayesian networks from binary data is proposed. The proposed method applies an entropy distillation operation over bounded groups of variables. A bias from the expected increase in randomness signals an underlaying statistical dependence between the inputs. The detected higher-order dependencies are used to connect linked attributes in the Bayesian network in a single step.