Mean-Squared Accuracy of Good-Turing Estimator

14 Apr 2021  ·  Maciej Skorski ·

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 \emph{and} alphabet size. read more

PDF Abstract
No code implementations yet. Submit your code now

Datasets


  Add Datasets introduced or used in this paper

Results from the Paper


  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods


No methods listed for this paper. Add relevant methods here