INDEPENDENCE IN COMPLETE AND INCOMPLETE CAUSAL NETWORKS UNDER MAXIMUM ENTROPY
In an expert system having a consistent set of linear constraints it is known that the Method of Tribus may be used to determine a probability distribution which exhibits maximised entropy. The method is extended here to include independence constraints (Accommodation). The paper proceeds to discusses this extension, and its limitations, then goes on to advance a technique for determining a small set of independencies which can be added to the linear constraints required in a particular representation of an expert system called a causal network, so that the Maximum Entropy and Causal Networks methodologies give matching distributions (Emulation). This technique may also be applied in cases where no initial independencies are given and the linear constraints are incomplete, in order to provide an optimal ME fill-in for the missing information.
independence causal networks maximum entropy method of tribus missing information