Poisson group testing: A probabilistic model for nonadaptive streaming boolean compressed sensing
Poisson group testing: A probabilistic model for nonadaptive streaming boolean compressed sensing
复制标题
泊松群测试：非自适应流布尔压缩感知的概率模型
DOI：
10.1109/icassp.2014.6854218
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发表时间：
2014
期刊：
影响因子：
--
通讯作者：
O. Milenkovic
中科院分区：
文献类型：
--
作者：
A. Emad;O. Milenkovic
我们介绍了一种新型的概率组测试框架，称为泊松组测试，其中缺陷的数量遵循右截断的泊松分布。泊松模型适用于许多生物测试方案，其中假定受试者根据其到达时间和有缺陷的概率随时间降低而排序。我们的主要结果是信息理论的上限，以实现渐近差异为零的平均检测误差所需的最小测试次数。
We introduce a novel probabilistic group testing framework, termed Poisson group testing, in which the number of defectives follows a right-truncated Poisson distribution. The Poisson model applies to a number of biological testing scenarios, where the subjects are assumed to be ordered based on their arrival times and where the probability of being defective decreases with time. Our main result is an information-theoretic upper bound on the minimum number of tests required to achieve an average probability of detection error asymptotically converging to zero.