AAAI Publications, Twenty-Seventh AAAI Conference on Artificial Intelligence

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Symmetry-Aware Marginal Density Estimation
Mathias Niepert

Last modified: 2013-06-30


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.


probabilistic inference; graphical models' symmetry-aware inference; symmetry; lifted inference

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