Bayesian Model Calibration

Bayesian Model Calibration is a statistical framework that belongs to the class of Bayesian inference methods, distinguished by its incorporation of prior knowledge and observed data to systematically adjust model parameters and quantify uncertainties in order to align model predictions with empirical observations.

415

Publications

29.3K

Citations

1.3K

Authors

603

Institutions

Publications per year

2017–2026

190

Authors

1.3K

  1. MV
    Marcel van Oijen

    UK Centre for Ecology & Hydrology, United Kingdom

    12 publications · 1K citations

    12

    h-index

  2. SM
    Sankaran Mahadevan

    Vanderbilt University, United States

    9 publications · 301 citations

    9

    h-index

  3. RC
    Ruchi Choudhary

    University of Cambridge, United Kingdom

    7 publications · 258 citations

    7

    h-index

  4. JR
    Jaideep Ray

    Sandia National Laboratories California, United States

    6 publications · 176 citations

    6

    h-index

  5. AG
    Andrew Gelman

    Columbia University, United States

    5 publications · 355 citations

    5

    h-index

1–5 of 1.3K

Venues

  1. Water Resources Research

    19 publications · 1.1K citations

    18

    h-index

  2. Journal of the American Statistical Association

    12 publications · 1.4K citations

    12

    h-index

  3. Energy and Buildings

    9 publications · 720 citations

    9

    h-index

  4. Technometrics

    7 publications · 239 citations

    7

    h-index

  5. Ecological Modelling

    7 publications · 421 citations

    7

    h-index

1–5