arXiv:2508.00937v3 Announce Type: replace-cross 
Abstract: We present a general approach to visualizing uncertainty in static 2-D statistical graphics. If we treat a visualization as a function of its underlying quantities, uncertainty in those quantities induces a distribution over images. We show how to aggregate these images into a single visualization that represents the uncertainty. The approach can be viewed as a generalization of sample-based approaches that use overlay. Notably, standard representations, such as confidence intervals and bands, emerge with their usual coverage guarantees without being explicitly quantified or visualized. As a proof of concept, we implement our approach in the IID setting using resampling, provided as an open-source Python library. Because the approach operates directly on images, the user needs only to supply the data and the code for visualizing the quantities of interest without uncertainty. Through several examples, we show how both familiar and novel forms of uncertainty visualization can be created. The implementation is not only a practical validation of the underlying theory but also an immediately usable tool that can complement existing uncertainty-visualization libraries.

تم تقديم نهج جديد لتصور عدم اليقين في الرسوم البيانية الإحصائية الثابتة ثنائية الأبعاد، حيث يتم اعتبار التصورات كوظائف للكمّيات الأساسية. تجمع هذه الطريقة بين الصور لتمثيل عدم اليقين، مما يسمح بظهور تمثيلات قياسية مثل فترات الثقة بشكل طبيعي دون الحاجة إلى قياس صريح.

Se ha introducido un nuevo enfoque para visualizar la incertidumbre en gráficos estadísticos 2-D estáticos, tratando las visualizaciones como funciones de cantidades subyacentes. Este método agrega imágenes para representar la incertidumbre, permitiendo que representaciones estándar como los intervalos de confianza emerjan de forma natural sin cuantificación explícita.

Une nouvelle approche pour visualiser l'incertitude dans les graphiques statistiques 2-D statiques a été introduite, considérant les visualisations comme des fonctions de quantités sous-jacentes. Cette méthode agrège les images pour représenter l'incertitude, permettant à des représentations standard comme les intervalles de confiance d'émerger naturellement sans quantification explicite.

A new approach to visualizing uncertainty in static 2-D statistical graphics has been introduced, treating visualizations as functions of underlying quantities. This method aggregates images to represent uncertainty, allowing for standard representations like confidence intervals to emerge naturally without explicit quantification.

A General Approach to Visualizing Uncertainty in Statistical Graphics

Was this article worth reading? Share it

LucidQuery AI

The Visualizer

drawcharts

Pixo.art

Tattoo Visualizer

AIPortalX

Ready to build your own newsroom?