Posterior Normality

Posterior Normality is a theoretical construct in Bayesian statistics that characterizes the asymptotic convergence of a posterior distribution to a normal distribution under regularity conditions, distinguished by its reliance on the concentration of posterior density around the true parameter value as sample size increases.

81

Publications

4.5K

Citations

114

Authors

79

Institutions

Publications per year

2017–2026

8

Authors

114

  1. BC
    Bertrand Clarke

    University of British Columbia, Canada

    3 publications · 62 citations

    3

    h-index

  2. CN
    Carl N. Morris

    RAND Corporation, United States

    2 publications · 233 citations

    2

    h-index

  3. SG
    Subhashis Ghosal

    North Carolina State University, United States

    2 publications · 133 citations

    2

    h-index

  4. EH
    Elizabeth H. Slate

    Medical University of South Carolina, United States

    2 publications · 51 citations

    2

    h-index

  5. BE
    Bradley Efron

    Stanford University, United States

    2 publications · 233 citations

    2

    h-index

1–5 of 114

Venues

  1. 12

    h-index

  2. The Annals of Statistics

    7 publications · 220 citations

    7

    h-index

  3. 5

    h-index

  4. Journal of Statistical Planning and Inference

    4 publications · 167 citations

    4

    h-index

  5. The Annals of Mathematical Statistics

    4 publications · 344 citations

    4

    h-index

1–5