Uncertainty in Artificial Intelligence
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Constraint Propagation with Imprecise Conditional Probabilities
Stephane Amarger, Didier Dubois, Henri Prade
Abstract:
An approach to reasoning with default rules where the proportion of exceptions, or more generally the probability of encountering an exception, can be at least roughly assessed is presented. It is based on local uncertainty propagation rules which provide the best bracketing of a conditional probability of interest from the knowledge of the bracketing of some other conditional probabilities. A procedure that uses two such propagation rules repeatedly is proposed in order to estimate any simple conditional probability of interest from the available knowledge. The iterative procedure, that does not require independence assumptions, looks promising with respect to the linear programming method. Improved bounds for conditional probabilities are given when independence assumptions hold.
Keywords:
Pages: 26-34
PS Link:
PDF Link: /papers/91/p26-amarger.pdf
BibTex:
@INPROCEEDINGS{Amarger91,
AUTHOR = "Stephane Amarger and Didier Dubois and Henri Prade",
TITLE = "Constraint Propagation with Imprecise Conditional Probabilities",
BOOKTITLE = "Proceedings of the Seventh Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-91)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Mateo, CA",
YEAR = "1991",
PAGES = "26--34"
}


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