MESA: Maximum Entropy by Simulated Annealing
This addresses the need for accurate probabilistic reasoning in systems with contradictory statements, though it appears incremental as an extension of maximum entropy methods.
The paper tackles the problem of deriving a joint probability distribution from probabilistic rules and facts, particularly handling reliability and conflicts, resulting in a method that processes large inference networks with high precision.
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.