The principle of minimum cross-entropy (ME-principle) is often used in the AI-areas of knowledge representation and uncertain reasoning as an elegant and powerful tool to build up complete probability distributions when only partial knowledge is available. The inputs it may be applied to are a prior distribution P and some new information R, and it yields as a result the one distribution P* that satisfies R and is closest to P in an information-theoretic sense. More generally, it provides a ”best” solution to the problem ”How to adjust P to R?”