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This paper considers the noisy group testing problem where among a largepopulation of items some are defective. The goal is to identify all defectiveitems by testing groups of items, with the minimum possible number of tests.The focus of this work is on the practical settings with a limited number ofitems rather than the asymptotic regime. In the current literature, beliefpropagation has been shown to be effective in recovering defective items fromthe test results. In this work, we adopt two variants of the belief propagationalgorithm for the noisy group testing problem. These algorithms have been usedsuccessfully in the decoding of low-density parity-check codes. We perform anexperimental study and using extensive simulations we show that thesealgorithms achieve higher success probability, lower false-negative, andfalse-positive rates compared to the traditional belief propagation algorithm.For instance, our results show that the proposed algorithms can reduce thefalse-negative rate by about $50\%$ (or more) when compared to the traditionalBP algorithm, under the combinatorial model. Moreover, under the probabilisticmodel, this reduction in the false-negative rate increases to about $80\%$ forthe tested cases.

Scheduling Improves the Performance of Belief Propagation for Noisy Group Testing