Bayesian statistics emphasizes the importance of prior distributions, yet finding an appropriate one is practically challenging. When multiple sample results are taken regarding the frequency of the same event, these samples may be influenced by different selection effects. In the absence of suitable prior distributions to correct for these selection effects, it is necessary to exclude outlier sample results to avoid compromising the final result. However, defining outliers based on different thresholds may change the result, which makes the result less persuasive. This work proposes a definition of outliers without the need to set thresholds.

翻译：贝叶斯统计强调先验分布的重要性，然而在实践中找到合适的先验分布具有挑战性。当针对同一事件频率获得多个采样结果时，这些样本可能受到不同选择效应的影响。在缺乏合适先验分布来校正这些选择效应的情况下，有必要排除异常样本结果以避免影响最终结果的准确性。然而，基于不同阈值定义异常值可能改变最终结果，这会降低结论的说服力。本研究提出了一种无需设定阈值的异常值定义方法。