Uncertainty in Artificial Intelligence
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Computing Probability Intervals Under Independency Constraints
Linda van der Gaag
Many AI researchers argue that probability theory is only capable of dealing with uncertainty in situations where a full specification of a joint probability distribution is available, and conclude that it is not suitable for application in knowledge-based systems. Probability intervals, however, constitute a means for expressing incompleteness of information. We present a method for computing such probability intervals for probabilities of interest from a partial specification of a joint probability distribution. Our method improves on earlier approaches by allowing for independency relationships between statistical variables to be exploited.
Keywords: null
Pages: 457-466
PS Link:
PDF Link: /papers/90/p457-van_der_gaag.pdf
@INPROCEEDINGS{van der Gaag90,
AUTHOR = "Linda van der Gaag ",
TITLE = "Computing Probability Intervals Under Independency Constraints",
BOOKTITLE = "Uncertainty in Artificial Intelligence 6 Annual Conference on Uncertainty in Artificial Intelligence (UAI-90)",
PUBLISHER = "Elsevier Science",
ADDRESS = "Amsterdam, NL",
YEAR = "1990",
PAGES = "457--466"

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