Some Prior(s) Experience Necessary: Templates for Getting Started With Bayesian Analysis
Some Prior(s) Experience Necessary: Templates for Getting Started With Bayesian Analysis
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一些必要的先前经验：贝叶斯分析入门模板
DOI：
10.1145/3290605.3300709
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Resnick, Paul
中科院分区：
文献类型：
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作者：
Phelan, Chanda;Hullman, Jessica;Kay, Matthew;Resnick, Paul
贝叶斯统计分析近年来引起了人们的关注，包括在人机交互领域。贝叶斯方法比传统统计有几个优点，包括产生具有更直观解释的结果。尽管兴趣日益浓厚，CHI 中很少有论文使用贝叶斯分析。学习贝叶斯统计的现有工具需要投入大量时间，因此很难随意探索贝叶斯方法。在这里，我们提出了一个降低探索障碍的工具：一组 R 代码模板，可指导贝叶斯新手完成首次分析。这些模板是针对 CHI 量身定制的，支持最近 CHI 论文中最常见的分析。在用户研究中，我们发现这些模板易于理解和使用。然而，我们发现没有统计背景的参与者对其使用并没有信心。我们的贡献共同提供了简洁的分析工具和实证结果，以帮助理解和解决在人机交互中使用贝叶斯分析的障碍。
Bayesian statistical analysis has gained attention in recent years, including in HCI. The Bayesian approach has several advantages over traditional statistics, including producing results with more intuitive interpretations. Despite growing interest, few papers in CHI use Bayesian analysis. Existing tools to learn Bayesian statistics require significant time investment, making it difficult to casually explore Bayesian methods. Here, we present a tool that lowers the barrier to exploration: a set of R code templates that guide Bayesian novices through their first analysis. The templates are tailored to CHI, supporting analyses found to be most common in recent CHI papers. In a user study, we found that the templates were easy to understand and use. However, we found that participants without a statistical background were not confident in their use. Together our contributions provide a concise analysis tool and empirical results for understanding and addressing barriers to using Bayesian analysis in HCI.