论文标题
基于贝叶斯后预测的小组测试中的主动合并设计
Active pooling design in group testing based on Bayesian posterior prediction
论文作者
论文摘要
在确定人群中受感染的患者时，小组测试是减少测试数量并纠正测试错误的有效方法。在小组测试程序中，对从患者收集的标本池进行了测试，那里的池数量低于患者。小组测试的性能在很大程度上取决于用于从测试结果中推断受感染患者的池和算法的设计。在本文中，贝叶斯推论的框架中提出了基于预测分布的池的自适应设计方法。与在预先确定的随机池上进行的组测试相比，使用信念传播算法执行的提出的方法会更准确地鉴定感染患者。
In identifying infected patients in a population, group testing is an effective method to reduce the number of tests and correct the test errors. In the group testing procedure, tests are performed on pools of specimens collected from patients, where the number of pools is lower than that of patients. The performance of group testing heavily depends on the design of pools and algorithms that are used in inferring the infected patients from the test outcomes. In this paper, an adaptive design method of pools based on the predictive distribution is proposed in the framework of Bayesian inference. The proposed method executed using the belief propagation algorithm results in more accurate identification of the infected patients, as compared to the group testing performed on random pools determined in advance.
