Bayesian Uncertainty Estimation

Bayesian Uncertainty Estimation is a probabilistic framework for quantifying measurement uncertainty by updating prior beliefs with observed data through Bayes' theorem to derive a posterior distribution that characterizes the uncertainty in model parameters or predictions.

526

Publications

32.1K

Citations

1.4K

Authors

601

Institutions

Publications per year

2017–2026

210

Authors

1.4K

  1. CE
    Clemens Elster

    Physikalisch-Technische Bundesanstalt, Germany

    8 publications · 230 citations

    8

    h-index

  2. IL
    Ignacio Lira

    Pontificia Universidad Católica de Chile, Chile

    8 publications · 282 citations

    8

    h-index

  3. CP
    Costas Papadimitriou

    University of Thessaly, Greece

    8 publications · 295 citations

    8

    h-index

  4. SM
    Sankaran Mahadevan

    Vanderbilt University, United States

    6 publications · 423 citations

    6

    h-index

  5. WW
    Wolfgang Wöger

    Physikalisch-Technische Bundesanstalt, Germany

    6 publications · 387 citations

    6

    h-index

1–5 of 1.4K

Venues

  1. Metrologia

    33 publications · 1.2K citations

    18

    h-index

  2. arXiv (Cornell University)

    Repository · 30 publications · 1.6K citations

    18

    h-index

  3. Mechanical Systems and Signal Processing

    MSSP · 12 publications · 383 citations

    12

    h-index

  4. Reliability Engineering & System Safety

    11 publications · 394 citations

    11

    h-index

  5. Water Resources Research

    9 publications · 1.9K citations

    9

    h-index

1–5