Uncertainty in Artificial Intelligence
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Probabilistic Inference in Influence Diagrams
Nevin Zhang
Abstract:
This paper is about reducing influence diagram (ID) evaluation into Bayesian network (BN) inference problems. Such reduction is interesting because it enables one to readily use one's favorite BN inference algorithm to efficiently evaluate IDs. Two such reduction methods have been proposed previously (Cooper 1988, Shachter and Peot 1992). This paper proposes a new method. The BN inference problems induced by the mew method are much easier to solve than those induced by the two previous methods.
Keywords: Decision analysis, influence diagrams, Bayesian networks, inference.
Pages: 514-522
PS Link: ftp://ftp.cs.ust.hk/~lzhang/uai98id.ps.gz
PDF Link: /papers/98/p514-zhang.pdf
BibTex:
@INPROCEEDINGS{Zhang98,
AUTHOR = "Nevin Zhang ",
TITLE = "Probabilistic Inference in Influence Diagrams",
BOOKTITLE = "Proceedings of the Fourteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-98)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1998",
PAGES = "514--522"
}


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