2012/06/15 by Marc Maier, Maier, Marc, David Jensen +1
Computer Science · #Bayesian Modeling and Causal Inference #Cognitive Science and Mapping
paper · pdf · doi:10.48550/arxiv.1206.3536
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.