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- W1530964327 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." @default.
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- W1530964327 date "1996-01-01" @default.
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- W1530964327 title "Learning Bayesian Networks is NP-Complete" @default.
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- W1530964327 doi "https://doi.org/10.1007/978-1-4612-2404-4_12" @default.
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