Uncertainty in Artificial Intelligence
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Conditions Under Which Conditional Independence and Scoring Methods Lead to Identical Selection of Bayesian Network Models
Robert Cowell
Abstract:
It is often stated in papers tackling the task of inferring Bayesian network structures from data that there are these two distinct approaches: (i) Apply conditional independence tests when testing for the presence or otherwise of edges; (ii) Search the model space using a scoring metric. Here I argue that for complete data and a given node ordering this division is a myth, by showing that cross entropy methods for checking conditional independence are mathematically identical to methods based upon discriminating between models by their overall goodness-of-fit logarithmic scores.
Keywords:
Pages: 91-97
PS Link:
PDF Link: /papers/01/p91-cowell.pdf
BibTex:
@INPROCEEDINGS{Cowell01,
AUTHOR = "Robert Cowell ",
TITLE = "Conditions Under Which Conditional Independence and Scoring Methods Lead to Identical Selection of Bayesian Network Models",
BOOKTITLE = "Proceedings of the Seventeenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-01)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "2001",
PAGES = "91--97"
}


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