论文标题
Bernoulli随机变量平均值的紧密相对估计
Tight relative estimation in the mean of Bernoulli random variables
论文作者
论文摘要
给定伯努利随机变量流，请考虑在指定的相对误差中估计随机变量的平均值，并具有指定的失败概率。到目前为止，伽马伯诺利近似方案（GBAS）是使用最少数量的平均样品来实现此目标的方法。在这项工作中，引入了一种新方法，当平均值远离零时，该方法更快。该过程使用两个阶段的过程以及一些简单的不等式，以在错误概率上获得严格的界限。
Given a stream of Bernoulli random variables, consider the problem of estimating the mean of the random variable within a specified relative error with a specified probability of failure. Until now, the Gamma Bernoulli Approximation Scheme (GBAS) was the method that accomplished this goal using the smallest number of average samples. In this work, a new method is introduced that is faster when the mean is bounded away from zero. The process uses a two-stage process together with some simple inequalities to get rigorous bounds on the error probability.
