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arXiv 2104.02015cs.HC
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为了设计探索性数据分析的界面,我们需要图形推理理论

To design interfaces for exploratory data analysis, we need theories of graphical inference

Jessica Hullman, Andrew Gelman

摘要:

计算机科学和统计学的研究和发展已经产生了越来越复杂的软件界面,用于交互式和探索性分析,并针对容易的模式发现和数据暴露进行了优化。但是,强调探索而不是其他分析阶段的设计哲学可能会混淆对灵活性的需求,并得出结论,即探索性可视化分析本质上是无模型的,不能形式化。我们描述了如何在没有人类统计推断理论的基础上,探索性视觉分析的研究可能会导致矛盾的界面目标和不确定性的表示,从而阻止用户进行有效的推断。我们讨论了如何在贝叶斯统计框架中的模型检查的概念,探索性和验证性分析,以及这种理解如何与其他提出的图形推理理论。将交互式分析视为由模型检查驱动,这为围绕探索性和可视化分析的软件和实证研究提出了新的方向。例如,系统应该能够指定和显式地将数据与空值和其他参考分布进行比较,并更好地表示不确定性。贝叶斯和其他图形推理理论的含义应该根据人们交互分析的结果进行测试,以推动理论的发展。

英文摘要:

Research and development in computer science and statistics have produced increasingly sophisticated software interfaces for interactive and exploratory analysis, optimized for easy pattern finding and data exposure. But design philosophies that emphasize exploration over other phases of analysis risk confusing a need for flexibility with a conclusion that exploratory visual analysis is inherently model-free and cannot be formalized. We describe how without a grounding in theories of human statistical inference, research in exploratory visual analysis can lead to contradictory interface objectives and representations of uncertainty that can discourage users from drawing valid inferences. We discuss how the concept of a model check in a Bayesian statistical framework unites exploratory and confirmatory analysis, and how this understanding relates to other proposed theories of graphical inference. Viewing interactive analysis as driven by model checks suggests new directions for software and empirical research around exploratory and visual analysis. For example, systems should enable specifying and explicitly comparing data to null and other reference distributions and better representations of uncertainty. Implications of Bayesian and other theories of graphical inference should be tested against outcomes of interactive analysis by people to drive theory development.

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