In this formalism, the likelihood of a proposition is represented as a subinterval of the until interval. The smaller this interval, the more precisely the probability of that proposition is known. The result is a new mass distribution representing their consensus. Next, this pooled information is extended from those propositions it directly bears upon, to those it indirectly bears upon, and converted to the interval representation. Prior probabilities, frequently difficult or impossible to collect in artificial intelligence domains, but required by most other systems of inexact reasoning, are not needed.
Dependency graphs are formal representations of dependency relations.
The freedom to express partial information makes dependency graphs suitable for modeling the degrees of belief one should accord a group of related propositions based on evidential information, a suitable host for Shafer's theory.