Work

Threshold sampling

Year: 2024

Type: article

Abstract: We consider the problem of sampling elements with some desired property from a large set, without testing the property of interest, but with the (probabilistic) assurance to have at least one match among the random sample. Like in ranked set sampling, we consider an infinite population under study, whose properties of interest are too expensive and/or time-consuming to measure. Unlike RSS, we are void of a ranking mechanism, so our sampling is done entirely blind. We show how it is nonetheless doable to assure, with controllably large likelihood, to either have at least one of the interesting elements in a random sample, or, contrarily, sample with the likewise assurance of not having one of the interesting elements in the sample. Our technique utilizes density bounds for distributions and threshold functions from random graph theory.

Source: Theoretical Computer Science

DOI: 10.1016/j.tcs.2024.114847

Authors: Stefan Rass, Max-Julian Jakobitsch, Stefan Haan, Moritz Hiebler

Institutions: Johannes Kepler University of Linz, University of Klagenfurt

Language: English

Topic: Complexity and Algorithms in Graphs

Subfield: Computational Theory and Mathematics

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $2,800

Funder: Austrian Science Fund