Name
Abstract | ||
|---|---|---|
Animated representations of outcomes drawn from distributions (hypothetical outcome plots, or HOPs) are used in the media and other public venues to communicate uncertainty. HOPs greatly improve multivariate probability estimation over conventional static uncertainty visualizations and leverage the ability of the visual system to quickly, accurately, and automatically process the summary statistic... |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TVCG.2018.2864909 | IEEE Transactions on Visualization and Computer Graphics |
Keywords | Field | DocType |
Uncertainty,Data visualization,Visualization,Bars,Task analysis,Observers,Encoding | Time series,Data visualization,Public domain,Task analysis,Multivariate statistics,Visualization,Computer science,Theoretical computer science,Artificial intelligence,Perception,Machine learning,Encoding (memory) | Journal |
Volume | Issue | ISSN |
25 | 1 | 1077-2626 |
Citations | PageRank | References |
15 | 0.67 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alex M. Kale | 1 | 27 | 3.18 |
| Francis Nguyen | 2 | 17 | 1.09 |
| Matthew Kay | 3 | 451 | 30.42 |
| Jessica Hullman | 4 | 477 | 26.51 |