Nonparametric regression models have recently surged in their power and popularity, accompanying the trend of increasing dataset size and complexity. While these models have proven their predictive ability in empirical settings, they are often difficult to interpret and do not address the underlying inferential goals of the analyst or decision maker. In this paper, we propose a modular two-stage approach for creating parsimonious, interpretable summaries of complex models which allow freedom in the choice of modeling technique and the inferential target. In the first stage a flexible model is fit which is believed to be as accurate as possible. In the second stage, lower-dimensional summaries are constructed by projecting draws from the distribution onto simpler structures. These summaries naturally come with valid Bayesian uncertainty estimates. Further, since we use the data only once to move from prior to posterior, these uncertainty estimates remain valid across multiple summaries and after iteratively refining a summary. We apply our method and demonstrate its strengths across a range of simulated and real datasets. Code to reproduce the examples shown is avaiable at github.com/spencerwoody/ghost

中文翻译：

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通过低维后验总结来解释模型

伴随着数据集规模和复杂性不断增加的趋势，非参数回归模型的力量和流行度最近激增。虽然这些模型已经证明了它们在经验环境中的预测能力，但它们通常难以解释，并且不能解决分析师或决策者的潜在推理目标。在本文中，我们提出了一种模块化的两阶段方法，用于创建复杂模型的简洁、可解释的摘要，允许自由选择建模技术和推理目标。在第一阶段，拟合一个灵活的模型，该模型被认为是尽可能准确的。在第二阶段，通过将分布的绘图投影到更简单的结构上来构建低维摘要。这些总结自然带有有效的贝叶斯不确定性估计。此外，由于我们只使用一次数据从先验到后验，因此这些不确定性估计在多个摘要中以及在迭代提炼摘要之后仍然有效。我们应用我们的方法并在一系列模拟和真实数据集上展示其优势。可在 github.com/spencerwoody/ghost 上获得重现所示示例的代码