# A General Framework for Symmetric Property Estimation

arXiv: https://arxiv.org/abs/2003.00844 · HTML: https://mlsanity.com/paper/2003.00844/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Mar 2020.

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

- [On the Efficient Implementation of High Accuracy Optimality of Profile Maximum Likelihood](https://mlsanity.com/paper/2210.06728/) (2022)

## Abstract (arXiv)

In this paper we provide a general framework for estimating symmetric properties of distributions from i.i.d. samples. For a broad class of symmetric properties we identify the easy region where empirical estimation works and the difficult region where more complex estimators are required. We show that by approximately computing the profile maximum likelihood (PML) distribution citeADOS16 in this difficult region we obtain a symmetric property estimation framework that is sample complexity optimal for many properties in a broader parameter regime than previous universal estimation approaches based on PML. The resulting algorithms based on these pseudo PML distributions are also more practical.