Bayesian Uncertainty Analysis

Bayesian Uncertainty Analysis is a statistical inference framework that quantifies uncertainty in model parameters and predictions by updating prior beliefs with observed data through Bayes' theorem.

624

Publications

43.2K

Citations

1.5K

Authors

684

Institutions

Publications per year

2017–2026

199

Authors

1.5K

  1. SM
    Sankaran Mahadevan

    Vanderbilt University, United States

    13 publications · 531 citations

    12

    h-index

  2. IL
    Ignacio Lira

    Pontificia Universidad Católica de Chile, Chile

    8 publications · 282 citations

    8

    h-index

  3. CE
    Clemens Elster

    Physikalisch-Technische Bundesanstalt, Germany

    8 publications · 223 citations

    8

    h-index

  4. MB
    Michael Beer

    University of Liverpool, United Kingdom

    7 publications · 222 citations

    7

    h-index

  5. MJ
    Mitchell J. Small

    Carnegie Mellon University, United States

    6 publications · 361 citations

    6

    h-index

1–5 of 1.5K

Venues

  1. Water Resources Research

    20 publications · 3.1K citations

    19

    h-index

  2. Metrologia

    29 publications · 1.1K citations

    18

    h-index

  3. Reliability Engineering & System Safety

    16 publications · 1.1K citations

    15

    h-index

  4. arXiv (Cornell University)

    Repository · 16 publications · 863 citations

    14

    h-index

  5. Risk Analysis

    15 publications · 745 citations

    13

    h-index

1–5