Uncertainty in Artificial Intelligence
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Identifying the Relevant Nodes Without Learning the Model
Jose Pena, Roland Nilsson, Johan Björkegren, Jesper Tegnér
Abstract:
We propose a method to identify all the nodes that are relevant to compute all the conditional probability distributions for a given set of nodes. Our method is simple, effcient, consistent, and does not require learning a Bayesian network first. Therefore, our method can be applied to high-dimensional databases, e.g. gene expression databases.
Keywords:
Pages: 367-374
PS Link:
PDF Link: /papers/06/p367-pena.pdf
BibTex:
@INPROCEEDINGS{Pena06,
AUTHOR = "Jose Pena and Roland Nilsson and Johan Björkegren and Jesper Tegnér",
TITLE = "Identifying the Relevant Nodes Without Learning the Model",
BOOKTITLE = "Proceedings of the Twenty-Second Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-06)",
PUBLISHER = "AUAI Press",
ADDRESS = "Arlington, Virginia",
YEAR = "2006",
PAGES = "367--374"
}


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