Learning Bayesian Networks Does Not Have to Be NP-Hard

  • Norbert Dojer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4162)


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.


Bayesian Network Optimal Network Minimal Description Length Dynamic Bayesian Network Bayesian Belief Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Norbert Dojer
    • 1
  1. 1.Institute of InformaticsWarsaw UniversityWarszawaPoland

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