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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... (Update)
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BibTeX entry: (Update)
D.M. Chickering, "Learning bayesian networks is np-complete", Submitted to Proceedings of AI and Statistics, 1995. http://citeseer.ist.psu.edu/article/chickering96learning.html More
@misc{ chickering95learning,
author = "D. Chickering",
title = "Learning bayesian networks is np-complete",
text = "D.M. Chickering, Learning bayesian networks is np-complete, Submitted to
Proceedings of AI and Statistics, 1995.",
year = "1995",
url = "citeseer.ist.psu.edu/article/chickering96learning.html" }
Citations (may not include all citations):4212 Computers and intractability: A guide to the theory of NP-co.. (context) - Garey, Johnson - 1979
416 A Bayesian method for the induction of probabilistic network.. (context) - Cooper, Herskovits - 1992
12 Learning and robust learning of product distributions (context) - Hoffgen - 1993
4 A Transformational characterization of Bayesian network stru.. (context) - Chickering - 1995
4 Learning discrete Bayesian networks (context) - Heckerman, Geiger et al. - 1995
2 Some NP-complete problems on graphs (context) - Garvil - 1977
The graph only includes citing articles where the year of publication is known.
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