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Add to EdgeIdentifying Independence in Relational Models
Apr 15, 2013
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
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