  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.
