Symmetry-Aware Marginal Density Estimation

AAAI 2013 pp. 725-731

doi:10.1609/AAAI.V27I1.8621 /aaai/2013/niepert2013aaai-symmetry/

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.

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Cite

Text

Niepert. "Symmetry-Aware Marginal Density Estimation." AAAI Conference on Artificial Intelligence, 2013. doi:10.1609/AAAI.V27I1.8621

Markdown

[Niepert. "Symmetry-Aware Marginal Density Estimation." AAAI Conference on Artificial Intelligence, 2013.](https://mlanthology.org/aaai/2013/niepert2013aaai-symmetry/) doi:10.1609/AAAI.V27I1.8621

BibTeX

@inproceedings{niepert2013aaai-symmetry,
  title     = {{Symmetry-Aware Marginal Density Estimation}},
  author    = {Niepert, Mathias},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2013},
  pages     = {725-731},
  doi       = {10.1609/AAAI.V27I1.8621},
  url       = {https://mlanthology.org/aaai/2013/niepert2013aaai-symmetry/}
}