Probabilistic Visual Analytics

Probabilistic Visual Analytics is a visual analytics approach that integrates probabilistic models to represent and reason about uncertainty in data, distinguishing itself by enabling interactive visualization and analysis of probabilistic outcomes and their underlying distributions.

9

Publications

459

Citations

32

Authors

25

Institutions

Publications per year

2017–2026

4

Authors

32

  1. YW
    Yingcai Wu

    Zhejiang University, China

    1 publications · 195 citations

    1

    h-index

  2. NF
    Nivan Ferreira

    New York University, United States

    1 publications · 25 citations

    1

    h-index

  3. FL
    Fanshun Lv

    Hangzhou Dianzi University, China

    1 publications · 13 citations

    1

    h-index

  4. DF
    Danyel Fisher

    Microsoft (United States), United States

    1 publications · 25 citations

    1

    h-index

  5. ME
    M. Eduard Gröller

    TU Wien, Austria

    1 publications · 18 citations

    1

    h-index

1–5 of 32

Venues

  1. IEEE Transactions on Visualization and Computer Graphics

    TVCG · 2 publications · 70 citations

    2

    h-index

  2. IEEE Access

    1 publications · 13 citations

    1

    h-index

  3. Visual Informatics

    1 publications · 42 citations

    1

    h-index

  4. Journal of Computer Science and Technology

    1 publications · 195 citations

    1

    h-index

  5. Computer Graphics Forum

    1 publications · 18 citations

    1

    h-index

1–5