Identifying Independence in Relational Models

Abstract

The rules of d-separation provide a framework for deriving conditional independence facts from model structure. However, this theory only applies to simple directed graphical models. We introduce relational d-separation, a theory for deriving conditional independence in relational models. We provide a sound, complete, and computationally efficient method for relational d-separation, and we present empirical results that demonstrate effectiveness.

This paper has been revised and expanded. See "Reasoning about Independence in Probabilistic Models of Relational Data" http://arxiv.org/abs/1302.4381