An Odds Ratio Based Inference Engine

TL;DR

This paper introduces an inference engine based on odds ratios that uses multidimensional contingency tables to make uncertain inferences without assuming independence among evidence, maintaining dependencies as evidence accumulates.

Contribution

It presents a novel method for uncertain inference using contingency tables that preserves evidence dependencies without relying on independence assumptions.

Findings

Enables inference without independence assumptions

Maintains evidence dependencies during inference

Calculates conditional probabilities directly from joint probabilities

Abstract

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.