论文标题
使用交叉验证的贝叶斯因子测试两个密度的平等
Use of Cross-validation Bayes Factors to Test Equality of Two Densities
论文作者
论文摘要
我们提出了一个非参数的两样本贝叶斯测试，用于检查两个数据集是否共享共同的分布。该测试利用数据分裂的想法，并且不需要先验，就像其他非参数贝叶斯程序一样。我们提供的证据表明，与基于Pólya树的方法相比，新程序提供了更多稳定的贝叶斯因素。有些令人惊讶的是，当两个分布相同时，提议的贝叶斯因素的行为通常优于pólya树贝叶斯因子的行为。我们通过证明其一致性，进行仿真研究并将测试应用于Higgs Boson数据来展示测试的有效性。
We propose a non-parametric, two-sample Bayesian test for checking whether or not two data sets share a common distribution. The test makes use of data splitting ideas and does not require priors for high-dimensional parameter vectors as do other nonparametric Bayesian procedures. We provide evidence that the new procedure provides more stable Bayes factors than do methods based on Pólya trees. Somewhat surprisingly, the behavior of the proposed Bayes factors when the two distributions are the same is usually superior to that of Pólya tree Bayes factors. We showcase the effectiveness of the test by proving its consistency, conducting a simulation study and applying the test to Higgs boson data.
