Computing Probability Intervals Under Independency Constraints
  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.
