Computing Probability Intervals Under Independency Constraints
arXiv:1304.1140
Abstract
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.
Appears in Proceedings of the Sixth Conference on Uncertainty in Artificial Intelligence (UAI1990)
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- Probabilistic Deduction with Conditional Constraints over Basic Events
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