Kernelized Stein discrepancy (KSD) is a score-based discrepancy widely used in goodness-of-fit tests. It can be applied even when the target distribution has an unknown normalising factor, such as in Bayesian analysis. We show theoretically and empirically that the KSD test can suffer from low power when the target and the alternative distributions have the same well-separated modes but differ in mixing proportions. We propose to perturb the observed sample via Markov transition kernels, with respect to which the target distribution is invariant. This allows us to then employ the KSD test on the perturbed sample. We provide numerical evidence that with suitably chosen transition kernels the proposed approach can lead to substantially higher power than the KSD test.

翻译：核化斯坦因散度（KSD）是一种基于得分的差异度量，广泛应用于拟合优度检验。即使在目标分布具有未知归一化因子（如贝叶斯分析中）的情况下，该方法仍可适用。本文从理论和实验两方面证明：当目标分布与备择分布具有相同的良好分离模态但混合比例不同时，KSD检验的势可能较低。我们提出通过马尔可夫转移核对观测样本进行扰动，目标分布相对于该核保持不变。进而对扰动后的样本应用KSD检验。数值实验结果表明，在适当选择转移核的条件下，所提方法可获得比标准KSD检验显著更高的检验势。