Model Uncertainty Estimation

Model Uncertainty Estimation is a methodological framework within risk estimation that quantifies the uncertainty associated with the structure, assumptions, and parameters of a predictive model, distinguishing it from other sources of uncertainty by focusing specifically on the reliability of the model itself rather than data or measurement variability.

539

Publications

31.2K

Citations

1.6K

Authors

701

Institutions

Publications per year

2017–2026

196

Authors

1.6K

  1. BL
    Baoding Liu

    Tsinghua University, China

    7 publications · 773 citations

    7

    h-index

  2. SM
    Sankaran Mahadevan

    Vanderbilt University, United States

    6 publications · 412 citations

    6

    h-index

  3. CJ
    Christopher J. Roy

    Virginia Tech, United States

    5 publications · 186 citations

    5

    h-index

  4. WL
    William L. Oberkampf

    Sandia National Laboratories, United States

    5 publications · 497 citations

    5

    h-index

  5. LL
    Lindsay Lee

    University of Leeds, United Kingdom

    5 publications · 282 citations

    5

    h-index

1–5 of 1.6K

Venues

  1. Reliability Engineering & System Safety

    20 publications · 1.9K citations

    19

    h-index

  2. arXiv (Cornell University)

    Repository · 15 publications · 475 citations

    12

    h-index

  3. Water Resources Research

    11 publications · 1.3K citations

    11

    h-index

  4. SSRN Electronic Journal

    Repository · 10 publications · 495 citations

    10

    h-index

  5. Risk Analysis

    9 publications · 382 citations

    9

    h-index

1–5