Prediction Interval Visualization

Prediction Interval Visualization is a visual representation method that depicts the range of possible outcomes for a predicted value at a given confidence level, distinguishing itself from other uncertainty visualizations by specifically encoding the variability and uncertainty inherent in future observations rather than model parameters or point estimates.

4

Publications

216

Citations

11

Authors

8

Institutions

Publications per year

2017–2026

1

Authors

11

  1. LJ
    L. J. Wei

    Harvard University, United States

    1 publications · 24 citations

    1

    h-index

  2. KP
    K. P. Sudheer

    Indian Institute of Technology Madras, India

    1 publications · 36 citations

    1

    h-index

  3. DG
    Daniel G. Goldstein

    Microsoft (United States), United States

    1 publications · 80 citations

    1

    h-index

  4. HU
    Hajime Uno

    Dana-Farber Cancer Institute, United States

    1 publications · 24 citations

    1

    h-index

  5. LL
    Lingling Li

    Harvard Pilgrim Health Care, United States

    1 publications · 24 citations

    1

    h-index

1–5 of 11

Venues

  1. Statistics in Biopharmaceutical Research

    SBR · 1 publications · 24 citations

    1

    h-index

  2. Applied Cognitive Psychology

    ACP · 1 publications · 76 citations

    1

    h-index

  3. Modeling Earth Systems and Environment

    MESE · 1 publications · 36 citations

    1

    h-index

1–3