论文标题
自适应抽样以估计分布：贝叶斯上限绑定方法
Adaptive Sampling for Estimating Distributions: A Bayesian Upper Confidence Bound Approach
论文作者
论文摘要
考虑到均匀估算概率质量功能（PMF）的自适应抽样问题。采样策略的性能是根据最坏情况的平均平方误差来衡量的。提出了现有上层置信界（UCB）方法的贝叶斯变体。分析表明，该贝叶斯变体的性能并不比现有方法差。贝叶斯环境中PMFS上的后验分布允许对上限界限进行更严格的计算，从而在实践中带来显着的性能增长。使用这种方法，提出了自适应抽样方案，用于估计位置和种族等各个群体中的SARS-COV-2血清阳性。使用从洛杉矶县的血清阳性调查获得的数据讨论了该策略的有效性。
The problem of adaptive sampling for estimating probability mass functions (pmf) uniformly well is considered. Performance of the sampling strategy is measured in terms of the worst-case mean squared error. A Bayesian variant of the existing upper confidence bound (UCB) based approaches is proposed. It is shown analytically that the performance of this Bayesian variant is no worse than the existing approaches. The posterior distribution on the pmfs in the Bayesian setting allows for a tighter computation of upper confidence bounds which leads to significant performance gains in practice. Using this approach, adaptive sampling protocols are proposed for estimating SARS-CoV-2 seroprevalence in various groups such as location and ethnicity. The effectiveness of this strategy is discussed using data obtained from a seroprevalence survey in Los Angeles county.
