用图像叠加法统一可视化统计图中的不确定性，无需手动设计置信区间。
A General Approach to Visualizing Uncertainty in Statistical Graphics
- 将可视化视为数据函数，通过重采样生成不确定性图像分布。
- 自动呈现置信区间等标准表示，且保持原有覆盖率保证。
- 开源工具可直接集成到现有绘图代码中，适合数据科学家快速使用。
我们提出一种通用方法，用于在静态二维统计图形中可视化不确定性。若将可视化视为其底层数据的函数，则数据中的不确定性会引发图像分布。我们展示如何将这些图像聚合为单一可视化以表征不确定性。该方法可视为基于样本的叠加方法的推广。值得注意的是，标准表示如置信区间和带状图在不显式量化或绘制的情况下自然出现，并保持其原有的覆盖率保证。作为概念验证，我们在独立同分布（IID）场景下使用重采样实现该方法，提供了一个开源Python库。由于方法直接作用于图像，用户只需提供数据和感兴趣的量的绘图代码，无需处理不确定性。通过多个示例，我们展示了既熟悉又新颖的不确定性可视化形式。该实现不仅是理论的有效验证，也是一个即插即用的实用工具，可补充现有不确定性可视化库。
原文摘要 · Abstract (English)
We present a general approach to visualizing uncertainty in static 2-D statistical graphics. If we treat a visualization as a function of its underlying quantities, uncertainty in those quantities induces a distribution over images. We show how to aggregate these images into a single visualization that represents the uncertainty. The approach can be viewed as a generalization of sample-based approaches that use overlay. Notably, standard representations, such as confidence intervals and bands, emerge with their usual coverage guarantees without being explicitly quantified or visualized. As a proof of concept, we implement our approach in the IID setting using resampling, provided as an open-source Python library. Because the approach operates directly on images, the user needs only to supply the data and the code for visualizing the quantities of interest without uncertainty. Through several examples, we show how both familiar and novel forms of uncertainty visualization can be created. The implementation is not only a practical validation of the underlying theory but also an immediately usable tool that can complement existing uncertainty-visualization libraries.
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