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 :

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