Robust Bayesian Modeling

Robust Bayesian Modeling is a Bayesian modeling framework that explicitly accounts for potential deviations from assumed model specifications, such as outliers, model misspecifications, or influential observations, by incorporating flexible priors, heavy-tailed distributions, or sensitivity analyses to ensure inferential reliability across a range of plausible data-generating mechanisms.

265

Publications

16.6K

Citations

597

Authors

344

Institutions

Publications per year

2017–2026

71

Authors

597

  1. JO
    James O. Berger

    Purdue University West Lafayette, United States

    11 publications · 1.6K citations

    10

    h-index

  2. FR
    Fabrizio Ruggeri

    Istituto di Matematica Applicata e Tecnologie Informatiche, Italy

    8 publications · 838 citations

    8

    h-index

  3. LW
    Larry Wasserman

    Carnegie Mellon University, United States

    6 publications · 524 citations

    6

    h-index

  4. EM
    Elı́as Moreno

    Universidad de Granada, Spain

    5 publications · 746 citations

    5

    h-index

  5. JB
    Joseph B. Kadane

    Carnegie Mellon University, United States

    5 publications · 962 citations

    5

    h-index

1–5 of 597

Venues

  1. Journal of Statistical Planning and Inference

    18 publications · 841 citations

    15

    h-index

  2. Journal of the American Statistical Association

    13 publications · 700 citations

    11

    h-index

  3. The Annals of Statistics

    11 publications · 1.1K citations

    11

    h-index

  4. arXiv (Cornell University)

    Repository · 13 publications · 674 citations

    11

    h-index

  5. Bayesian Analysis

    8 publications · 254 citations

    8

    h-index

1–5