Symmetry-aware marginal density estimation

Abstract

The Rao-Blackwell theorem is utilized to analyze and improve the scalability of inference in large probabilistic models that exhibit symmetries. A novel marginal density estimator is introduced and shown both analytically and empirically to outperform standard estimators by several orders of magnitude. The developed theory and algorithms apply to a broad class of probabilistic models including statistical relational models considered not susceptible to lifted probabilistic inference Copyright © 2013, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.