Predictive Posterior Analysis

Predictive Posterior Analysis is a statistical framework within Bayesian inference that characterizes the distribution of future observations by integrating over the posterior distribution of model parameters, thereby distinguishing itself from prior predictive methods through its dependence on observed data to update parameter uncertainty.

102

Publications

8.6K

Citations

333

Authors

213

Institutions

Publications per year

2017–2026

30

Authors

333

  1. HS
    Hal S. Stern

    University of California, Irvine, United States

    4 publications · 2.4K citations

    4

    h-index

  2. TE
    Trivellore E. Raghunathan

    University of Michigan, United States

    3 publications · 251 citations

    3

    h-index

  3. HH
    Herbert Hoijtink

    Utrecht University, Netherlands

    3 publications · 127 citations

    3

    h-index

  4. RC
    Robert C. Thomson

    University of Hawaii System, United States

    3 publications · 201 citations

    3

    h-index

  5. NC
    Noel Cressie

    Iowa State University, United States

    2 publications · 154 citations

    2

    h-index

1–5 of 333

Venues

  1. Statistics in Medicine

    8 publications · 487 citations

    8

    h-index

  2. 7

    h-index

  3. arXiv (Cornell University)

    Repository · 3 publications · 53 citations

    3

    h-index

  4. Environmetrics

    3 publications · 215 citations

    3

    h-index

  5. PLoS ONE

    2 publications · 40 citations

    2

    h-index

1–5