论文标题
近似贝叶斯推断的失真估计值
Distortion estimates for approximate Bayesian inference
论文作者
论文摘要
目前关于贝叶斯推理后近似值的文献提供了许多替代方法。我们选择的近似方案在观察到的数据上是否效果很好？最好的现有通用诊断工具通过查看在数据空间上平均的性能，或者缺乏诊断细节，从而处理此类问题。但是，如果近似对大多数数据不利，但是擅长观察到的数据，那么我们可能会丢弃有用的近似值。我们在观察到的数据上给出了后近似值的图形诊断。我们估计一个“失真图”，该“失真图”作用于近似后部的单变量边缘，使其更接近确切的后部，而无需求助于确切的后部。
Current literature on posterior approximation for Bayesian inference offers many alternative methods. Does our chosen approximation scheme work well on the observed data? The best existing generic diagnostic tools treating this kind of question by looking at performance averaged over data space, or otherwise lack diagnostic detail. However, if the approximation is bad for most data, but good at the observed data, then we may discard a useful approximation. We give graphical diagnostics for posterior approximation at the observed data. We estimate a "distortion map" that acts on univariate marginals of the approximate posterior to move them closer to the exact posterior, without recourse to the exact posterior.
