Bayesian Confidence Updating

Bayesian Confidence Updating is a computational framework within confidence calibration that formalizes the refinement of probabilistic beliefs through Bayesian inference, distinguishing itself by systematically integrating prior confidence estimates with observed evidence to produce posterior confidence levels in a coherent, normative manner.

66

Publications

3.2K

Citations

205

Authors

146

Institutions

Publications per year

2017–2026

29

Authors

205

  1. TL
    Tony L. Schmitz

    University of Florida, United States

    2 publications · 119 citations

    2

    h-index

  2. JK
    Jaydeep Karandikar

    Oak Ridge National Laboratory, United States

    2 publications · 119 citations

    2

    h-index

  3. AE
    Ali E. Abbas

    University of Illinois Urbana-Champaign, United States

    2 publications · 119 citations

    2

    h-index

  4. DS
    Dániel Straub

    Technical University of Munich, Germany

    1 publications · 134 citations

    1

    h-index

  5. JD
    Justin D. Yeakel

    University of California, Merced, United States

    1 publications · 53 citations

    1

    h-index

1–5 of 205

Venues

  1. Applied Mathematical Modelling

    2 publications · 45 citations

    2

    h-index

  2. Reliability Engineering & System Safety

    2 publications · 78 citations

    2

    h-index

  3. Ocean Engineering

    2 publications · 40 citations

    2

    h-index

  4. Precision Engineering

    2 publications · 119 citations

    2

    h-index

  5. PLoS Computational Biology

    2 publications · 97 citations

    2

    h-index

1–5