交互是否有助于用户更好地理解概率模型的结构？
Does Interacting Help Users Better Understand the Structure of Probabilistic Models?
摘要：
尽管人们对概率建模方法和学习工具的可用性越来越感兴趣，但没有或很少有统计背景的人对使用它们感到犹豫。有必要的工具，沟通概率模型，经验不足的用户更直观地帮助他们建立，验证，有效地使用或信任概率模型。用户的概率模型的理解是至关重要的，在这些情况下，交互式可视化可以增强it.Although有各种研究评估贝叶斯推理的交互性和可用的工具可视化的样本为基础的分布，我们专注于评估用户的概率模型的结构理解的交互效果。我们基于交互式配对图进行了一项用户研究，以可视化模型的分布并以图形方式调节样本空间。我们的研究结果表明，相互作用组的理解的改善是最明显的更奇特的结构，如层次模型或不熟悉的参数化相比，静态组。随着推断信息的细节增加，交互不会导致显著更长的响应时间。最后，互动提高了用户的信心。
英文摘要：
Despite growing interest in probabilistic modeling approaches and availability of learning tools, people with no or less statistical background feel hesitant to use them. There is need for tools for communicating probabilistic models to less experienced users more intuitively to help them build, validate, use effectively or trust probabilistic models. Users' comprehension of probabilistic models is vital in these cases and interactive visualizations could enhance it. Although there are various studies evaluating interactivity in Bayesian reasoning and available tools for visualizing the sample-based distributions, we focus specifically on evaluating the effect of interaction on users' comprehension of probabilistic models' structure. We conducted a user study based on our Interactive Pair Plot for visualizing models' distribution and conditioning the sample space graphically. Our results suggest that improvements in the understanding of the interaction group are most pronounced for more exotic structures, such as hierarchical models or unfamiliar parameterizations in comparison to the static group. As the detail of the inferred information increases, interaction does not lead to considerably longer response times. Finally, interaction improves users' confidence.
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。
