# Symmetry-Aware Marginal Density Estimation

arXiv: https://arxiv.org/abs/1304.2694 · HTML: https://mlsanity.com/paper/1304.2694/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Apr 2013.

## Built on by (most cited first, 1 of 1)

- [Towards Efficient MCMC Sampling in Bayesian Neural Networks by Exploiting Symmetry](https://mlsanity.com/paper/2304.02902/) (2023)

## Abstract (arXiv)

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.