# Efficient and accurate group testing via Belief Propagation: an empirical study

arXiv: https://arxiv.org/abs/2105.07882 · HTML: https://mlsanity.com/paper/2105.07882/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 53 references that are in the map.
- First posted on arXiv 13 May 2021.

## Builds on (most cited first, 5 of 5)

- [Statistical physics of inference: Thresholds and algorithms](https://mlsanity.com/paper/1511.02476/) (2015)
- [Decoding from Pooled Data: Phase Transitions of Message Passing](https://mlsanity.com/paper/1702.02279/) (2017)
- [Noisy Adaptive Group Testing using Bayesian Sequential Experimental Design](https://mlsanity.com/paper/2004.12508/) (2020)
- [Recent Advances in Scalable Network Generation](https://mlsanity.com/paper/2003.00736/) (2020)
- [Group Testing for COVID-19: How to Stop Worrying and Test More](https://mlsanity.com/paper/2004.06306/) (2020)

## Abstract (arXiv)

The group testing problem asks for efficient pooling schemes and algorithms that allow to screen moderately large numbers of samples for rare infections. The goal is to accurately identify the infected samples while conducting the least possible number of tests. Exploring the use of techniques centred around the Belief Propagation message passing algorithm, we suggest a new test design that significantly increases the accuracy of the results. The new design comes with Belief Propagation as an efficient inference algorithm. Aiming for results on practical rather than asymptotic problem sizes, we conduct an experimental study.