评估贝叶斯模型可视化
Evaluating Bayesian Model Visualisations
摘要：
概率模型为越来越广泛的商业和政策决策提供信息，这些决策最终由人们做出。最近的算法，计算和软件框架的开发进展促进了贝叶斯概率模型的扩散，它通过它们的联合分布而不是点估计来计算未观察到的参数。虽然它们可以使决策者能够探索复杂的查询，并在理论上执行假设式调节，但需要适当的可视化和交互式工具，以最大限度地提高用户的理解力，并在不确定性下做出理性决策。在本文中，提出了一个协议的贝叶斯模型可视化的定量评估，并介绍了实现该协议的软件框架，以支持标准化的评估实践，并促进再现性。我们说明了用户研究的评估和分析工作流程，探讨是否使箱形图和假设结果图交互可以增加理解或合理性，并得出结论，为研究人员希望在未来进行类似的研究设计准则。
英文摘要：
Probabilistic models inform an increasingly broad range of business and policy decisions ultimately made by people. Recent algorithmic, computational, and software framework development progress facilitate the proliferation of Bayesian probabilistic models, which characterise unobserved parameters by their joint distribution instead of point estimates. While they can empower decision makers to explore complex queries and to perform what-if-style conditioning in theory, suitable visualisations and interactive tools are needed to maximise users' comprehension and rational decision making under uncertainty. In this paper, propose a protocol for quantitative evaluation of Bayesian model visualisations and introduce a software framework implementing this protocol to support standardisation in evaluation practice and facilitate reproducibility. We illustrate the evaluation and analysis workflow on a user study that explores whether making Boxplots and Hypothetical Outcome Plots interactive can increase comprehension or rationality and conclude with design guidelines for researchers looking to conduct similar studies in the future.
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。
