Optimum Detection of Defective Elements in Non-Adaptive Group Testing
Information Theory
2021-02-11 v1 math.IT
Abstract
We explore the problem of deriving a posteriori probabilities of being defective for the members of a population in the non-adaptive group testing framework. Both noiseless and noisy testing models are addressed. The technique, which relies of a trellis representation of the test constraints, can be applied efficiently to moderate-size populations. The complexity of the approach is discussed and numerical results on the false positive probability vs. false negative probability trade-off are presented.
Cite
@article{arxiv.2102.05508,
title = {Optimum Detection of Defective Elements in Non-Adaptive Group Testing},
author = {Gianluigi Liva and Enrico Paolini and Marco Chiani},
journal= {arXiv preprint arXiv:2102.05508},
year = {2021}
}
Comments
To be presented at the special session on Data Science for COVID-19 at CISS 2021