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

Report Date:

2014-01-06

Pagination or Media Count:

61.0

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.

Subject Categories:

  • Statistics and Probability

Distribution Statement:

APPROVED FOR PUBLIC RELEASE