Bensema Kevin, Gosink Luke, Obermaier Harald, Joy Kenneth I
IEEE Trans Vis Comput Graph. 2016 Oct;22(10):2289-2299. doi: 10.1109/TVCG.2015.2507569. Epub 2015 Dec 10.
Advances in computational power now enable domain scientists to address conceptual and parametric uncertainty by running simulations multiple times in order to sufficiently sample the uncertain input space. While this approach helps address conceptual and parametric uncertainties, the ensemble datasets produced by this technique present a special challenge to visualization researchers as the ensemble dataset records a distribution of possible values for each location in the domain. Contemporary visualization approaches that rely solely on summary statistics (e.g., mean and variance) cannot convey the detailed information encoded in ensemble distributions that are paramount to ensemble analysis; summary statistics provide no information about modality classification and modality persistence. To address this problem, we propose a novel technique that classifies high-variance locations based on the modality of the distribution of ensemble predictions. Additionally, we develop a set of confidence metrics to inform the end-user of the quality of fit between the distribution at a given location and its assigned class. Finally, for the special application of evaluating the stability of bimodal regions, we develop local and regional metrics.
计算能力的进步现在使领域科学家能够通过多次运行模拟来解决概念性和参数不确定性问题，以便充分对不确定的输入空间进行采样。虽然这种方法有助于解决概念性和参数不确定性，但这种技术产生的集成数据集给可视化研究人员带来了特殊挑战，因为集成数据集记录了域中每个位置的可能值分布。仅依赖于汇总统计信息（例如均值和方差）的当代可视化方法无法传达集成分布中编码的详细信息，而这些信息对于集成分析至关重要；汇总统计信息不提供有关模态分类和模态持久性的信息。为了解决这个问题，我们提出了一种新颖的技术，该技术基于集成预测分布的模态对高方差位置进行分类。此外，我们开发了一组置信度指标，以告知最终用户给定位置的分布与其分配类别之间的拟合质量。最后，对于评估双峰区域稳定性的特殊应用，我们开发了局部和区域指标。
