Computing Probability Intervals Under Independency Constraints

TL;DR

This paper introduces a method to compute probability intervals from partial joint distributions by leveraging independency constraints, enhancing the expressiveness of uncertainty modeling in AI systems.

Contribution

It presents a novel approach that incorporates independency relationships to improve the computation of probability intervals from incomplete information.

Findings

Method effectively exploits independency constraints.

Improves accuracy of probability interval estimation.

Applicable to knowledge-based AI systems.

Abstract

Many AI researchers argue that probability theory is only capable of dealing with uncertainty in situations where a full specification of a joint probability distribution is available, and conclude that it is not suitable for application in knowledge-based systems. Probability intervals, however, constitute a means for expressing incompleteness of information. We present a method for computing such probability intervals for probabilities of interest from a partial specification of a joint probability distribution. Our method improves on earlier approaches by allowing for independency relationships between statistical variables to be exploited.