Bayesian Emulation

Bayesian Emulation is a statistical modeling technique that belongs to the class of surrogate models, distinguished by its use of Bayesian inference to construct a probabilistic approximation of a complex simulation model, enabling efficient uncertainty quantification and analysis.

52

Publications

3K

Citations

180

Authors

81

Institutions

Publications per year

2017–2026

24

Variants

Bayesian Emulators

Authors

180

  1. MG
    Michael Goldstein

    Durham University, United Kingdom

    6 publications · 510 citations

    6

    h-index

  2. IV
    Ian Vernon

    Durham University, United Kingdom

    4 publications · 385 citations

    4

    h-index

  3. MW
    Mike West

    Duke University, United States

    3 publications · 135 citations

    3

    h-index

  4. DJ
    Darren J. Wilkinson

    Newcastle University, United Kingdom

    3 publications · 119 citations

    3

    h-index

  5. JE
    Jeremy E. Oakley

    University of Sheffield, United Kingdom

    2 publications · 221 citations

    2

    h-index

1–5 of 180

Venues

  1. 4

    h-index

  2. Bayesian Analysis

    3 publications · 124 citations

    3

    h-index

  3. SIAM/ASA Journal on Uncertainty Quantification

    JUQ · 3 publications · 101 citations

    3

    h-index

  4. arXiv (Cornell University)

    Repository · 2 publications · 67 citations

    2

    h-index

  5. 2

    h-index

1–5