  Probabilistic reasoning systems combine different probabilistic rules and
probabilistic facts to arrive at the desired probability values of
consequences. In this paper we describe the MESA-algorithm (Maximum Entropy by
Simulated Annealing) that derives a joint distribution of variables or
propositions. It takes into account the reliability of probability values and
can resolve conflicts between contradictory statements. The joint distribution
is represented in terms of marginal distributions and therefore allows to
process large inference networks and to determine desired probability values
with high precision. The procedure derives a maximum entropy distribution
subject to the given constraints. It can be applied to inference networks of
arbitrary topology and may be extended into a number of directions.
