Ensemble Average Analysis of Non-Adaptive Group Testing with Sparse Pooling Graphs
Abstract
A combinatorial analysis of the false alarm (FA) and misdetection (MD) probabilities of non-adaptive group testing with sparse pooling graphs is developed. The analysis targets the combinatorial orthogonal matching pursuit and definite defective detection algorithms in the noiseless, non-quantitative setting. The approach follows an ensemble average perspective, where average FA/MD probabilities are computed for pooling graph ensembles with prescribed degree distributions. The accuracy of the analysis is demonstrated through numerical examples, showing that the proposed technique can be used to characterize the performance of non-adaptive group testing schemes based on sparse pooling graphs.
Cite
@article{arxiv.2507.20281,
title = {Ensemble Average Analysis of Non-Adaptive Group Testing with Sparse Pooling Graphs},
author = {Emna Ben Yacoub and Gianluigi Liva and Enrico Paolini and Marco Chiani},
journal= {arXiv preprint arXiv:2507.20281},
year = {2025}
}
Comments
To be presented at the 2025 International Symposium on Topics in Coding (ISTC)