Uncertainty in Artificial Intelligence
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Valuation Networks and Conditional Independence
Prakash Shenoy
Abstract:
Valuation networks have been proposed as graphical representations of valuation-based systems (VBSs). The VBS framework is able to capture many uncertainty calculi including probability theory, Dempster-Shafer's belief-function theory, Spohn's epistemic belief theory, and Zadeh's possibility theory. In this paper, we show how valuation networks encode conditional independence relations. For the probabilistic case, the class of probability models encoded by valuation networks includes undirected graph models, directed acyclic graph models, directed balloon graph models, and recursive causal graph models.
Keywords:
Pages: 191-199
PS Link:
PDF Link: /papers/93/p191-shenoy.pdf
BibTex:
@INPROCEEDINGS{Shenoy93,
AUTHOR = "Prakash Shenoy ",
TITLE = "Valuation Networks and Conditional Independence",
BOOKTITLE = "Proceedings of the Ninth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-93)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1993",
PAGES = "191--199"
}


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