Computing Probability Intervals Under Independency Constraints
This work addresses the challenge of handling incomplete information in probability theory for knowledge-based systems, representing an incremental improvement.
The paper tackles the problem of computing probability intervals from partial joint distribution specifications, improving on earlier approaches by exploiting independency relationships between variables.
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.