Learning bayesian networks does not have to be NP-Hard

Authors:
Norbert Dojer
Affiliations:
Institute of Informatics, Warsaw University, Warszawa, Poland
Venue:
MFCS'06 Proceedings of the 31st international conference on Mathematical Foundations of Computer Science
Year:
2006

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Abstract

We propose an algorithm for learning an optimal Bayesian network from data. Our method is addressed to biological applications, where usually datasets are small but sets of random variables are large. Moreover we assume that there is no need to examine the acyclicity of the graph. We provide polynomial bounds (with respect to the number of random variables) for time complexity of our algorithm for two generally used scoring criteria: Minimal Description Length and Bayesian-Dirichlet equivalence.