Accession Number : AD1042640


Title :   Reasoning about Independence in Probabilistic Models of Relational Data (Author's Manuscript)


Descriptive Note : Journal Article


Corporate Author : University of Massachusetts, Amherst Amherst United States


Personal Author(s) : Maier,Marc ; Marazopoulou,Katerina ; Jensen,David


Full Text : https://apps.dtic.mil/dtic/tr/fulltext/u2/1042640.pdf


Report Date : 06 Jan 2014


Pagination or Media Count : 61


Abstract : We extend the theory of d-separation to cases in which data instances are not independent and identically distributed. We show that applying the rules of d-separation directly to the structure of probabilistic models of relational data inaccurately infers conditional independence. We introduce relational d-separation, a theory for deriving conditional independence facts from relational models. We provide a new representation, the abstract ground graph, that enables a sound, complete, and computationally efficient method for answering d-separation queries about relational models, and we present empirical results that demonstrate effectiveness.


Descriptors :   probability distributions , probabilistic models , bayesian networks , relational databases


Subject Categories : Statistics and Probability


Distribution Statement : APPROVED FOR PUBLIC RELEASE