Dependence and Relevance: A probabilistic view
This work addresses foundational issues in probabilistic modeling for AI and expert systems, but it appears incremental as it builds on existing concepts without introducing new methods or broad applications.
The paper tackles the problem of clarifying probabilistic concepts of independence and their relevance to constructing similarity networks for acquiring probabilistic knowledge from experts, establishing precise relationships between Bayesian network connectedness and probabilistic relevance.
We examine three probabilistic concepts related to the sentence "two variables have no bearing on each other". We explore the relationships between these three concepts and establish their relevance to the process of constructing similarity networks---a tool for acquiring probabilistic knowledge from human experts. We also establish a precise relationship between connectedness in Bayesian networks and relevance in probability.