Examples

The following jupyter notebooks contain examples on how to use the library. Dimensionality reduction ========================

Uncertainty-aware multidimensional scaling

Load data, reduce the dimensionality with UAMDS, visualize the output

Uncertainty-aware principal component analysis

Load data, reduce the dimensionality with UAPCA, visualize the output

Uncertainty-aware PCA revisited

Apply the sample-based formulation of uncertainty-aware PCA to the example data of the paper “Uncertainty-Aware PCA Revisited”

Uncertainty-aware PCA for Gaussian mixture models

Project Gaussian mixture model distributions with wGMM-UAPCA and visualize the output

Interactive weight exploration for UAPCA

Interactively explore how different weight configurations affect UAPCA and wGMM-UAPCA projections

VIPurPCA

Propagate distributional uncertainty through the PCA pipeline with VIPurPCA on synthetic test distributions

Time series analysis

Uncertainty-aware seasonal trend decomposition with LOESS

Load data, visualize a time series, apply UASTL, visualize the output

Uncertainty-aware Fourier analysis

Perform an uncertainty-aware spectral analysis of an uncertain time series

Visualization techniques

Stippling

Visualize uncertain 2D projections using stippling

Working with data

Working with own data

Load data, create a distribution, visualize it

Provided example datasets

Overview of the ready-to-use toy datasets shipped with the library