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.

翻译：最佳测试统计数据的设计是常客统计数据和一些假想的最佳测试统计数据(如剖面图似似准比率)的关键任务。 通过扭转这一论点,我们可以找到剖面图概率比率,即使是在没有可能性的情况下,只有模拟器的样本,通过优化这些假想中的测试统计数据。 我们提出一种不设可能性的培训算法,在假设中产生与剖面图似准比率相等的测试统计数据。