Likelihood-based Calibration

Likelihood-Based Calibration is a model calibration method that belongs to the class of likelihood-based inference techniques, distinguished by its use of the likelihood function to assess and adjust the agreement between model predictions and observed data under uncertainty.

13

Publications

551

Citations

42

Authors

24

Institutions

Publications per year

2017–2026

1

Authors

42

  1. KK
    Klaus Keller

    Pennsylvania State University, United States

    1 publications · 12 citations

    1

    h-index

  2. RS
    Rolf Sundberg

    Stockholm University, Sweden

    1 publications · 96 citations

    1

    h-index

  3. PN
    Pierre N. Robillard

    Polytechnique Montréal, Canada

    1 publications · 27 citations

    1

    h-index

  4. WC
    Won Chang

    University of Cincinnati, United States

    1 publications · 12 citations

    1

    h-index

  5. PJ
    Philip J. Brown

    University of Kent, United Kingdom

    1 publications · 96 citations

    1

    h-index

1–5 of 42

Venues

  1. Statistica Sinica

    1 publications · 12 citations

    1

    h-index

  2. Technometrics

    1 publications · 18 citations

    1

    h-index

  3. Scientific Reports

    1 publications · 58 citations

    1

    h-index

  4. Engineering Geology

    1 publications · 88 citations

    1

    h-index

  5. 1

    h-index

1–5