Robust Bayesian Calibration

Robust Bayesian Calibration is a Bayesian calibration framework that extends standard Bayesian calibration by formally incorporating uncertainty in prior specifications and model assumptions to ensure posterior inferences and predictive performance remain stable and reliable under perturbations or misspecifications in the input data or model structure.

8

Publications

449

Citations

32

Authors

19

Institutions

Publications per year

2017–2026

5

Authors

32

  1. JL
    Julia Ling

    Stanford University, United States

    1 publications · 33 citations

    1

    h-index

  2. JS
    Jim Specht

    Australian Museum, Australia

    1 publications · 99 citations

    1

    h-index

  3. DS
    Daniel Simpson

    University of Toronto, Canada

    1 publications · 226 citations

    1

    h-index

  4. MH
    Mats Høglind

    Norwegian Institute of Bioeconomy Research, Norway

    1 publications · 13 citations

    1

    h-index

  5. LD
    Lawrence Dechant

    Sandia National Laboratories, United States

    1 publications · 33 citations

    1

    h-index

1–5 of 32

Venues

  1. AIAA Journal

    1 publications · 33 citations

    1

    h-index

  2. 1

    h-index

  3. SIAM Journal on Scientific Computing

    1 publications · 21 citations

    1

    h-index

  4. Ecological Modelling

    1 publications · 13 citations

    1

    h-index

  5. arXiv (Cornell University)

    Repository · 1 publications · 226 citations

    1

    h-index

1–5