Ambiguity Visualization

Ambiguity Visualization is a probabilistic visualization approach that specifically represents uncertainty arising from multiple plausible interpretations or non-unique outcomes in data, distinguishing itself by explicitly encoding epistemic indeterminacy rather than aleatorichance variation.

11

Publications

686

Citations

22

Authors

10

Institutions

Publications per year

2017–2026

3

Authors

22

  1. EG
    Edwin G. Boring

    Harvard University Press, United States

    1 publications · 177 citations

    1

    h-index

  2. PQ
    Paolo Quattrone

    University of Oxford, United Kingdom

    1 publications · 84 citations

    1

    h-index

  3. EA
    Eytan Adar

    University of Michigan, United States

    1 publications · 239 citations

    1

    h-index

  4. VM
    Vivien Marx

    The Nature Conservancy, United States

    1 publications · 11 citations

    1

    h-index

  5. ZL
    Zhicheng Liu

    Adobe Systems (United States), United States

    1 publications · 239 citations

    1

    h-index

1–5 of 22

Venues

  1. Medical Entomology and Zoology

    1 publications · 17 citations

    1

    h-index

  2. 1

    h-index

  3. Informatics

    1 publications · 35 citations

    1

    h-index

  4. Applied Optics

    1 publications · 19 citations

    1

    h-index

  5. Accounting and Business Research

    1 publications · 84 citations

    1

    h-index

1–5