FODAVA: Bayesian Analysis in Visual Analytics (BAVA)

本研究的目标是将视觉分析和贝叶斯统计这两个领域结合起来。目前，可视化显示了数据的僵化确定性转换，这种转换本质上将数据可视化与视觉合成分开。分析师不能操纵显示器来注入特定领域的知识，以正式评估他们的专家判断与数据的合并。然而，通过将数据转换的性质从确定性贝叶斯方法改变为概率贝叶斯方法，对显示的操作可以定量地解释。因此，开发了一种新的可视化模型，该模型提供可编辑的表示来促进分析师和数据之间的双向流动，该方法使分析师能够量化数据的不确定性，将专家的判断正式地纳入分析中，快速生成和检验新的假设，并允许多源和多尺度的数据贡献于一个数据展示。开发的软件与包括情报分析在内的多个领域的分析师一起应用和评估。虽然这项研究立即影响了分析师如何在大数据集中发现新信息，但这种方法也平稳地过渡到课堂上。贝叶斯课程目前仅限于高级研究生，但在更直观的层面上学习贝叶斯统计可以克服这一障碍。贝叶斯方法和视觉分析的结合使学生能够通过视觉表征和意义形成反馈回路来评估不同水平的先验信息对概率模型的影响。具有不同背景、兴趣和才能的学生，包括本科生和学术历史有限的代表不足的群体，现在将有机会学习21世纪的统计学。项目网站(http://infovis.cs.vt.edu/BAVA))将用于传播成果。

The goal of this research is to combine two areas, Visual Analytics and Bayesian Statistics. Currently, visualizations display inflexible deterministic transformations of data that inherently separate data visualization from visual synthesis. Analysts cannot manipulate displays to inject domain-specific knowledge to formally assess the merger of their expert judgment with the data. However, by changing the nature of the data transformation from deterministic to probabilistic Bayesian methods, manipulations to a display are possible to interpret quantitatively. Thus, a new visualization model is developed which offers editable representations to promote bidirectional flow between analyst and data.This approach enables analysts to quantify data uncertainty, formally include expert judgment into analyses, rapidly generate and test new hypotheses, and allows multi-source and multi-scale data to contribute to one data display. The developed software is applied and evaluated with analysts in multiple fields, including intelligence analysis. While the research immediately impacts how analysts discover new information in large datasets, the methodology also transitions smoothly into the classroom. Bayesian courses are currently limited to advanced graduate students, but learning Bayesian statistics at a more intuitive level can overcome this barrier. The combination of Bayesian methodology and visual analytics enables students to assess the impact of various levels of prior information on probability models through visual representations and the sense-making feedback loop. Students with varying backgrounds, interests, and talents, including undergraduates and underrepresented groups with limited academic history, will now have the opportunity to learn 21st century statistics. The project Web site (http://infovis.cs.vt.edu/BAVA) will be used for results dissemination.