论文标题
贝叶斯选择性推断：非信息先验
Bayesian Selective Inference: Non-informative Priors
论文作者
论文摘要
我们讨论使用数据选择的参数的贝叶斯推断。首先，我们对文献中有关正确选择的贝叶斯方法的现有立场进行了批判性分析。其次，我们提出了两种用于选择模型的非信息先验。在没有先验信息的情况下，可以使用这些先验来产生后验分布，并为所选参数提供精心校准的频繁推断。我们在几种情况下经验地测试了提议的先生。
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.
