Posterior Asymptotics

Posterior Asymptotics is a theoretical framework in Bayesian statistics that characterizes the limiting behavior of posterior distributions as the sample size grows, distinguishing itself by analyzing convergence rates and consistency properties under increasingly large data regimes.

325

Publications

17.5K

Citations

441

Authors

255

Institutions

Publications per year

2017–2026

23

Authors

441

  1. CP
    Christian P. Robert

    Université Paris Dauphine-PSL, France

    6 publications · 276 citations

    6

    h-index

  2. BC
    Bertrand Clarke

    University of British Columbia, Canada

    5 publications · 533 citations

    5

    h-index

  3. JP
    James P. Hobert

    University of Florida, United States

    5 publications · 130 citations

    5

    h-index

  4. JR
    Judith Rousseau

    Université Paris Dauphine-PSL, France

    5 publications · 449 citations

    5

    h-index

  5. AY
    Albert Y. Lo

    University at Buffalo, State University of New York, United States

    4 publications · 317 citations

    4

    h-index

1–5 of 441

Venues

  1. The Annals of Statistics

    46 publications · 3K citations

    25

    h-index

  2. 18

    h-index

  3. Journal of the American Statistical Association

    15 publications · 797 citations

    15

    h-index

  4. The Annals of Mathematical Statistics

    12 publications · 1.1K citations

    11

    h-index

  5. Communication in Statistics- Theory and Methods

    12 publications · 172 citations

    11

    h-index

1–5