Model Uncertainty

Model Uncertainty is the epistemic uncertainty arising from the selection of a statistical or machine learning model structure and its parameters, reflecting the inadequacy of the chosen model to fully capture the underlying data-generating process due to limitations in model form, assumptions, or representational capacity.

4.1K

Publications

265.2K

Citations

11K

Authors

2.8K

Institutions

Publications per year

2017–2026

1.6K

Authors

11K

  1. AE
    Adrian E. Raftery

    University of Washington, United States

    19 publications · 15.5K citations

    17

    h-index

  2. SM
    Sankaran Mahadevan

    Vanderbilt University, United States

    16 publications · 1.1K citations

    15

    h-index

  3. TN
    T. N. Palmer

    European Centre for Medium-Range Weather Forecasts, United Kingdom

    15 publications · 1.7K citations

    14

    h-index

  4. MM
    Manfred Morari

    ETH Zurich, Switzerland

    12 publications · 3.3K citations

    13

    h-index

  5. GI
    Gianluca Iaccarino

    Stanford University, United States

    12 publications · 473 citations

    12

    h-index

1–5 of 11K

Venues

  1. Water Resources Research

    84 publications · 6.7K citations

    48

    h-index

  2. SSRN Electronic Journal

    Repository · 107 publications · 4.1K citations

    36

    h-index

  3. arXiv (Cornell University)

    Repository · 90 publications · 12.1K citations

    35

    h-index

  4. IEEE Transactions on Automatic Control

    40 publications · 3.6K citations

    29

    h-index

  5. Reliability Engineering & System Safety

    37 publications · 3.7K citations

    26

    h-index

1–5