9 citations · 10 across the 2 of their papers we have counts for
4 papers
Adaptive Uncertainty Visualization With jk-Plots
Nikolaus Piccolotto, Daniel Pahr, Laura Lotteraner +2
An important task of uncertainty visualizations is to communicate how the number of available observations influences the precision of estimates, such as a Normal distribution's me…
Data Type Agnostic Visual Sensitivity Analysis
Nikolaus Piccolotto, Markus Bögl, Christoph Muehlmann +4
Modern science and industry rely on computational models for simulation, prediction, and data analysis. Spatial blind source separation (SBSS) is a model used to analyze spatial da…
Visual Parameter Selection for Spatial Blind Source Separation
Nikolaus Piccolotto, Markus Bögl, Christoph Muehlmann +3
Analysis of spatial multivariate data, i.e., measurements at irregularly-spaced locations, is a challenging topic in visualization and statistics alike. Such data are integral to m…
TBSSvis: Visual Analytics for Temporal Blind Source Separation
Nikolaus Piccolotto, Markus Bögl, Theresia Gschwandtner +4
Temporal Blind Source Separation (TBSS) is used to obtain the true underlying processes from noisy temporal multivariate data, such as electrocardiograms. TBSS has similarities to…