Here, we develop an objective Bayesian analysis for large-scale datasets. When Bayesian analysis is applied to large-scale datasets, the cut point that provides the posterior probability is usually determined following customs. In this work, we propose setting the cut point in an objective manner, which is determined so as to match the posterior null number with the estimated true null number. The posterior probability obtained using an objective cut point is relatively similar to the real false discovery rate (FDR), which facilitates control of the FDR level.

翻译：本文针对大规模数据集提出了一种客观贝叶斯分析方法。当贝叶斯分析应用于大规模数据集时，提供后验概率的截止点通常依据惯例确定。本研究中，我们提出以客观方式设定截止点，该截止点通过使后验零假设数量与估计的真实零假设数量相匹配来确定。采用客观截止点得到的后验概率与实际错误发现率（FDR）较为接近，从而有助于控制FDR水平。