Knowledge Integration for Conditional Probability Assessments
  In the probabilistic approach to uncertainty management the input knowledge
is usually represented by means of some probability distributions. In this
paper we assume that the input knowledge is given by two discrete conditional
probability distributions, represented by two stochastic matrices P and Q. The
consistency of the knowledge base is analyzed. Coherence conditions and
explicit formulas for the extension to marginal distributions are obtained in
some special cases.
