Bayesian Uncertainty Visualization

Bayesian Uncertainty Visualization is a visualization method that specifically represents epistemic and aleatoric uncertainties derived from Bayesian probabilistic models by encoding posterior distributions, credible intervals, or predictive uncertainties into visual constructs to support interpretation and decision-making under uncertainty.

16

Publications

610

Citations

43

Authors

21

Institutions

Publications per year

2017–2026

11

Authors

43

  1. KB
    Katharina Böcherer-Linder

    University of Kassel, Germany

    4 publications · 119 citations

    4

    h-index

  2. AE
    A. Eichler

    University of Vienna, Austria

    4 publications · 119 citations

    4

    h-index

  3. MK
    Matthew Kay

    University of Washington, United States

    2 publications · 192 citations

    2

    h-index

  4. MV
    Markus Vogel

    Heidelberg University of Education, Germany

    2 publications · 37 citations

    2

    h-index

  5. PK
    Paula Kayongo

    California University of Pennsylvania, United States

    1 publications · 39 citations

    1

    h-index

1–5 of 43

Venues

  1. IEEE Transactions on Visualization and Computer Graphics

    TVCG · 3 publications · 176 citations

    3

    h-index

  2. Frontiers in Psychology

    3 publications · 107 citations

    3

    h-index

  3. Visual Informatics

    1 publications · 19 citations

    1

    h-index

  4. BMC Bioinformatics

    1 publications · 11 citations

    1

    h-index

  5. The American Statistician

    1 publications · 85 citations

    1

    h-index

1–5