chapters
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 networkâ€”among those where each node has at most K parentsâ€”that has a relative posterior probability greater than a given constant is NP-complete, when the BDe metric is used.
chapter
121-130
https://scigraph.springernature.com/explorer/license/
en
https://link.springer.com/10.1007%2F978-1-4612-2404-4_12
true
2019-04-16T09:26
1996-01-01
Learning Bayesian Networks is NP-Complete
1996
readcube_id
4283ee04403c6bb28a1b293598ae4d1f89dbb69e85f419df8483a9bc970e8540
Hans-J.
Lenz
Chickering
David Maxwell
University of California Los Angeles
Computer Science Department, University of California, Los Angeles, USA
pub.1013962799
dimensions_id
Mathematical Sciences
978-1-4612-2404-4
Learning from Data
978-0-387-94736-5
Doug
Fisher
Springer New York
New York, NY
Springer Nature - SN SciGraph project
doi
10.1007/978-1-4612-2404-4_12
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