We discuss Bayesian inference for parameters selected using the data. First, we provide a critical analysis of the existing positions in the literature regarding the correct Bayesian approach under selection. Second, we propose two types of non-informative priors for selection models. These priors may be employed to produce a posterior distribution in the absence of prior information as well as to provide well-calibrated frequentist inference for the selected parameter. We test the proposed priors empirically in several scenarios.

翻译：我们讨论使用数据选择参数的贝叶斯推论。 首先,我们对文献中关于选择的正确贝叶斯方法的现有立场进行批判性分析。 其次,我们建议选择模型有两种非信息规范的前置物。 这些前置物可用于在没有事先信息的情况下产生后置物分布,并为选定的参数提供经充分校准的常客推论。 我们在若干情况下对拟议的前置物进行了经验测试。