Reasoning about Independence in Probabilistic Models of Relational Data (Author's Manuscript)
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.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jan 06, 2014
- Accession Number
- AD1042640
Entities
People
- David Jensen
- Katerina Marazopoulou
- Marc Maier
Organizations
- University of Massachusetts Amherst