# Mean-Squared Accuracy of Good-Turing Estimator

arXiv: https://arxiv.org/abs/2104.07029 · HTML: https://mlsanity.com/paper/2104.07029/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 55 references that are in the map.
- First posted on arXiv 14 Apr 2021.

## Builds on (most cited first, 4 of 4)

- [On the Impossibility of Learning the Missing Mass](https://mlsanity.com/paper/1503.03613/) (2015)
- [A Good-Turing estimator for feature allocation models](https://mlsanity.com/paper/1902.10490/) (2019)
- [Non-Bayesian Parametric Missing-Mass Estimation](https://mlsanity.com/paper/2101.04329/) (2021)
- [Cardinality Estimation Meets Good-Turing](https://mlsanity.com/paper/1508.06216/) (2015)

## Built on by (most cited first, 2 of 2)

- [Conformal Prediction Beyond the Seen: A Missing Mass Perspective for Uncertainty Quantification in Generative Models](https://mlsanity.com/paper/2506.05497/) (2025)
- [NOVA: Fundamental Limits of Knowledge Discovery Through AI](https://mlsanity.com/paper/2605.15219/) (2026)

## Abstract (arXiv)

The brilliant method due to Good and Turing allows for estimating objects not occurring in a sample. The problem, known under names "sample coverage" or "missing mass" goes back to their cryptographic work during WWII, but over years has found has many applications, including language modeling, inference in ecology and estimation of distribution properties. This work characterizes the maximal mean-squared error of the Good-Turing estimator, for any sample and alphabet size.