论文标题
较大的偏差，通过在Bernoulli样品上绘制的阳性池样品数量的伯努利样本中的阳性个体数量的渐近界限
Large deviations, asymptotic bounds on the number of positive individuals in a Bernoulli sample via the number of positive pool samples drawn on the bernoulli sample
论文作者
论文摘要
在本文中，我们为Bernoulli定义了\ emph {经验感染测量}，该\ emph {经验感染测量}计算了Bernoulli样品中的阳性（感染）数量（感染），而对于\ Emph {pool samples}，我们定义了经验池感染测量量，这计算了阳性（感染）泳池样品的数量。对于这种经验措施，我们证明了Bernoulli样品的联合大偏差原理。我们还发现，相对于样品大小，$ n $和\ emph {受感染的池样品的比例} \ emph {感染个体的比例}相对于池样本的数量，$ k（n）。$所有费率函数以相对的熵表示。
In this paper we define for a Bernoulli samples the \emph{ empirical infection measure}, which counts the number of positives (infections) in the Bernoulli sample and for the \emph{ pool samples} we define the empirical pool infection measure, which counts the number of positive (infected) pool samples. For this empirical measures we prove a joint large deviation principle for Bernoulli samples. We also found an asymptotic relationship between the \emph{ proportion of infected individuals } with respect to the samples size, $n$ and the \emph{ proportion of infected pool samples} with respect to the number of pool samples, $k(n).$ All rate functions are expressed in terms of relative entropies.
