An Odds Ratio Based Inference Engine
This addresses the challenge of handling dependencies in uncertain evidence for expert systems, though it appears incremental as it builds on existing contingency table approaches.
The paper tackles the problem of uncertain inference in expert systems by using multidimensional contingency tables to avoid assumptions of conditional independence or heuristic rules, resulting in a method that directly calculates conditional probabilities from updated joint probabilities.
Expert systems applications that involve uncertain inference can be represented by a multidimensional contingency table. These tables offer a general approach to inferring with uncertain evidence, because they can embody any form of association between any number of pieces of evidence and conclusions. (Simpler models may be required, however, if the number of pieces of evidence bearing on a conclusion is large.) This paper presents a method of using these tables to make uncertain inferences without assumptions of conditional independence among pieces of evidence or heuristic combining rules. As evidence is accumulated, new joint probabilities are calculated so as to maintain any dependencies among the pieces of evidence that are found in the contingency table. The new conditional probability of the conclusion is then calculated directly from these new joint probabilities and the conditional probabilities in the contingency table.