Uncertainty in Artificial Intelligence
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A Generalization of the Noisy-Or Model
Sampath Srinivas
The Noisy-Or model is convenient for describing a class of uncertain relationships in Bayesian networks [Pearl 1988]. Pearl describes the Noisy-Or model for Boolean variables. Here we generalize the model to nary input and output variables and to arbitrary functions other than the Boolean OR function. This generalization is a useful modeling aid for construction of Bayesian networks. We illustrate with some examples including digital circuit diagnosis and network reliability analysis.
Pages: 208-215
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PDF Link: /papers/93/p208-srinivas.pdf
AUTHOR = "Sampath Srinivas ",
TITLE = "A Generalization of the Noisy-Or Model",
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 = "208--215"

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