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Learning Bayesian Networks is NP-Complete

  • David Maxwell Chickering
Part of the Lecture Notes in Statistics book series (LNS, volume 112)

Abstract

Algorithms for learning Bayesian networks from data have two components: a scoring metric and a search procedure. The scoring metric computes a score reflecting the goodness-of-fit of the structure to the data. The search procedure tries to identify network structures with high scores. Heckerman et al. (1995) introduce a Bayesian metric, called the BDe metric, that computes the relative posterior probability of a network structure given data. In this paper, we show that the search problem of identifying a Bayesian network—among those where each node has at most K parents—that has a relative posterior probability greater than a given constant is NP-complete, when the BDe metric is used.

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References

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Copyright information

© Springer-Verlag New York, Inc. 1996

Authors and Affiliations

  • David Maxwell Chickering
    • 1
  1. 1.Computer Science DepartmentUniversity of CaliforniaLos AngelesUSA

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