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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© 1996 Springer-Verlag New York, Inc.
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Chickering, D.M. (1996). Learning Bayesian Networks is NP-Complete. In: Fisher, D., Lenz, HJ. (eds) Learning from Data. Lecture Notes in Statistics, vol 112. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-2404-4_12
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DOI: https://doi.org/10.1007/978-1-4612-2404-4_12
Publisher Name: Springer, New York, NY
Print ISBN: 978-0-387-94736-5
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