Bayesian Model Uncertainty

Bayesian Model Uncertainty is a formal framework within Bayesian econometrics that characterizes the uncertainty surrounding model specification by treating competing models as hypotheses with associated posterior probabilities, thereby distinguishing it from parameter uncertainty through its focus on model space variability.

840

Publications

81.4K

Citations

2.2K

Authors

949

Institutions

Publications per year

2017–2026

300

Authors

2.2K

  1. AE
    Adrian E. Raftery

    University of Washington, United States

    18 publications · 14.2K citations

    16

    h-index

  2. TS
    Theo S. Eicher

    University of Washington, United States

    10 publications · 912 citations

    10

    h-index

  3. MA
    Merlise A. Clyde

    Duke University, United States

    8 publications · 788 citations

    8

    h-index

  4. CT
    Charalambos Tsangarides

    International Monetary Fund, United States

    7 publications · 177 citations

    7

    h-index

  5. MY
    Ming Ye

    Florida State University, United States

    7 publications · 399 citations

    7

    h-index

1–5 of 2.2K

Venues

  1. arXiv (Cornell University)

    Repository · 34 publications · 9.2K citations

    24

    h-index

  2. Water Resources Research

    25 publications · 3.4K citations

    22

    h-index

  3. SSRN Electronic Journal

    Repository · 27 publications · 1.3K citations

    18

    h-index

  4. Risk Analysis

    14 publications · 600 citations

    12

    h-index

  5. Reliability Engineering & System Safety

    11 publications · 689 citations

    11

    h-index

1–5