论文标题
在存在滋扰参数的情况下学习最佳测试统计数据
Learning Optimal Test Statistics in the Presence of Nuisance Parameters
论文作者
论文摘要
最佳测试统计数据的设计是常见统计数据的关键任务，对于许多方案，最佳测试统计量（例如概要样本统计）是已知的。通过扭转这一论点，我们即使在无似然的情况下也可以找到配置文件的可能性比，在这些情况下，只有来自模拟器的样本可以通过在这些情况下优化测试统计量。我们提出了一种无似然培训算法，该算法产生的测试统计量相当于在已知后者最佳的情况下的轮廓可能性比。
The design of optimal test statistics is a key task in frequentist statistics and for a number of scenarios optimal test statistics such as the profile-likelihood ratio are known. By turning this argument around we can find the profile likelihood ratio even in likelihood-free cases, where only samples from a simulator are available, by optimizing a test statistic within those scenarios. We propose a likelihood-free training algorithm that produces test statistics that are equivalent to the profile likelihood ratios in cases where the latter is known to be optimal.
