VMC：一种用于可视化统计模型检查的语法
VMC: A Grammar for Visualizing Statistical Model Checks
AI总结：
VMC提出了一种用于统计模型检查的可视化语法，通过四个组件定义可视化，实现为R包，验证显示其能减少可视化编辑距离，专家访谈揭示了探索有效模型检查可视化的挑战与机遇。
摘要：
可视化在验证和改进统计模型中起着关键作用。然而，模型检查可视化的设计空间尚未被充分理解，这使得作者难以探索和指定有效的图形模型检查。VMC使用四个组件定义模型检查可视化：（1）从模型生成的可检查量分布的样本，包括新数据的预测分布和模型参数的分布；（2）对观测数据的转换以促进比较；（3）分布的视觉表示；以及（4）用于促进比较模型样本和观测数据的布局。我们贡献了VMC作为R包的实现。我们通过重现一组经典模型检查示例来验证VMC，并展示使用VMC生成模型检查如何相对于现有可视化工具包减少可视化之间的编辑距离。对三位使用VMC的专家建模者进行的访谈研究结果，突出了鼓励探索正确、有效的模型检查可视化所面临的挑战和机遇。
英文摘要：
Visualizations play a critical role in validating and improving statistical models. However, the design space of model check visualizations is not well understood, making it difficult for authors to explore and specify effective graphical model checks. VMC defines a model check visualization using four components: (1) samples of distributions of checkable quantities generated from the model, including predictive distributions for new data and distributions of model parameters; (2) transformations on observed data to facilitate comparison; (3) visual representations of distributions; and (4) layouts to facilitate comparing model samples and observed data. We contribute an implementation of VMC as an R package. We validate VMC by reproducing a set of canonical model check examples, and show how using VMC to generate model checks reduces the edit distance between visualizations relative to existing visualization toolkits. The findings of an interview study with three expert modelers who used VMC highlight challenges and opportunities for encouraging exploration of correct, effective model check visualizations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
