# Estimating Coverage in Streams via a Modified CVM Method

arXiv: https://arxiv.org/abs/2504.04567 · HTML: https://mlsanity.com/paper/2504.04567/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 3 references that are in the map.
- First posted on arXiv 6 Apr 2025.

## Builds on (most cited first, 1 of 1)

- [Distinct Elements in Streams: An Algorithm for the (Text) Book](https://mlsanity.com/paper/2301.10191/) (2023)

## Abstract (arXiv)

When individuals in a population can be classified in classes or categories, the coverage of a sample, C, is defined as the probability that a randomly selected individual from the population belongs to a class represented in the sample. Estimating coverage is challenging because C is not a fixed population parameter, but a property of the sample, and the task becomes more complex when the number of classes is unknown. Furthermore, this problem has not been addressed in scenarios where data arrive as a stream, under the constraint that only n elements can be stored at a time. In this paper, we propose a simple and efficient method to estimate C in streaming settings, based on a straightforward modification of the CVM algorithm, which is commonly used to estimate the number of distinct elements in a data stream.