Contextual Weak Independence in Bayesian Networks

TL;DR

This paper introduces and explores the concept of contextual weak independence (CWI) in Bayesian networks, a more general form of independence that enhances probabilistic reasoning and knowledge representation.

Contribution

It proposes CWI as a broader framework than contextual strong independence (CSI), provides axiomatizations for WI and CI, and demonstrates WI's role in ensuring consistency in granular probabilistic networks.

Findings

CWI generalizes CSI and WI in Bayesian networks.

Complete axiomatizations for WI and CI+WI are developed.

WI is necessary and sufficient for consistency in granular networks.

Abstract

It is well-known that the notion of (strong) conditional independence (CI) is too restrictive to capture independencies that only hold in certain contexts. This kind of contextual independency, called context-strong independence (CSI), can be used to facilitate the acquisition, representation, and inference of probabilistic knowledge. In this paper, we suggest the use of contextual weak independence (CWI) in Bayesian networks. It should be emphasized that the notion of CWI is a more general form of contextual independence than CSI. Furthermore, if the contextual strong independence holds for all contexts, then the notion of CSI becomes strong CI. On the other hand, if the weak contextual independence holds for all contexts, then the notion of CWI becomes weak independence (WI) nwhich is a more general noncontextual independency than strong CI. More importantly, complete axiomatizations are studied for both the class of WI and the class of CI and WI together. Finally, the interesting property of WI being a necessary and sufficient condition for ensuring consistency in granular probabilistic networks is shown.