Bayesian Model Misspecification

Bayesian Model Misspecification is a formal statistical condition in which the assumed parametric model class, though specified a priori within a Bayesian framework, does not contain the true data-generating distribution, thereby leading to asymptotically biased posterior inferences despite correct probabilistic updating.

84

Publications

5.2K

Citations

209

Authors

148

Institutions

Publications per year

2017–2026

29

Authors

209

  1. KS
    Karthik Sriram

    Indian Institute of Management Ahmedabad, India

    3 publications · 181 citations

    3

    h-index

  2. DT
    David T. Frazier

    Monash University, Australia

    3 publications · 92 citations

    3

    h-index

  3. PG
    Peter Grünwald

    Centrum Wiskunde & Informatica, Netherlands

    3 publications · 93 citations

    3

    h-index

  4. DR
    David Rossell

    Institute for Research in Biomedicine, Spain

    3 publications · 70 citations

    3

    h-index

  5. CD
    Christ D. Richmond

    MIT Lincoln Laboratory, United States

    2 publications · 177 citations

    2

    h-index

1–5 of 209

Venues

  1. arXiv (Cornell University)

    Repository · 6 publications · 115 citations

    6

    h-index

  2. Computational Statistics & Data Analysis

    CSDA · 5 publications · 91 citations

    5

    h-index

  3. Bayesian Analysis

    5 publications · 237 citations

    5

    h-index

  4. 4

    h-index

  5. The Annals of Mathematical Statistics

    2 publications · 67 citations

    2

    h-index

1–5