Informative Priors

Informative Priors is a type of prior distribution in Bayesian model building that incorporates substantial, specific knowledge about the parameters, thereby distinguishing itself from non-informative or weakly informative priors by significantly influencing the posterior inference through concentrated probability mass on plausible parameter values.

737

Publications

59.9K

Citations

1.9K

Authors

915

Institutions

Publications per year

2017–2026

249

Authors

1.9K

  1. JG
    Joseph G. Ibrahim

    University of North Carolina at Chapel Hill, United States

    8 publications · 965 citations

    8

    h-index

  2. AG
    Andrew Gelman

    Columbia University, United States

    7 publications · 7.7K citations

    7

    h-index

  3. MC
    Ming‐Hui Chen

    University of Connecticut, United States

    6 publications · 717 citations

    6

    h-index

  4. RT
    Rebecca Turner

    MRC Biostatistics Unit, United Kingdom

    6 publications · 701 citations

    6

    h-index

  5. DK
    Dipak K. Dey

    University of Connecticut, United States

    5 publications · 252 citations

    5

    h-index

1–5 of 1.9K

Venues

  1. Journal of the American Statistical Association

    28 publications · 2.1K citations

    21

    h-index

  2. Statistics in Medicine

    24 publications · 1.9K citations

    19

    h-index

  3. Bayesian Analysis

    15 publications · 10.4K citations

    14

    h-index

  4. PLoS ONE

    15 publications · 591 citations

    13

    h-index

  5. SSRN Electronic Journal

    Repository · 12 publications · 534 citations

    12

    h-index

1–5