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.

To appear in proceedings of AAAI 2013

References in corpus (5)

• Sum-Product Networks: A New Deep Architecture
• Counting Belief Propagation
• Improving Asymptotic Variance of MCMC Estimators: Non-reversible Chains are Better
• Markov Chains on Orbits of Permutation Groups
• Automorphism Groups of Graphical Models and Lifted Variational Inference

Cited by in corpus (1)

• Tractability through Exchangeability: A New Perspective on Efficient Probabilistic Inference