Bayesian Interval Estimation

Bayesian Interval Estimation is a statistical inference method that determines a range of credible values for an unknown parameter by applying Bayes' theorem to update prior beliefs with observed data, thereby distinguishing itself from frequentist interval estimation through its explicit incorporation of prior knowledge and probabilistic interpretation of parameters.

178

Publications

10.4K

Citations

380

Authors

234

Institutions

Publications per year

2017–2026

44

Authors

380

  1. BE
    Bradley Efron

    Stanford University, United States

    4 publications · 287 citations

    4

    h-index

  2. SN
    Sa‐Aat Niwitpong

    King Mongkut's University of Technology North Bangkok, Thailand

    4 publications · 69 citations

    4

    h-index

  3. YY
    Ying Yuan

    The University of Texas MD Anderson Cancer Center, United States

    4 publications · 695 citations

    4

    h-index

  4. LJ
    Lawrence Joseph

    McGill University, Canada

    3 publications · 307 citations

    3

    h-index

  5. PG
    Paul Gustafson

    University of British Columbia, Canada

    2 publications · 149 citations

    3

    h-index

1–5 of 380

Venues

  1. Journal of the American Statistical Association

    12 publications · 583 citations

    11

    h-index

  2. Journal of Statistical Computation and Simulation

    JSCS · 9 publications · 212 citations

    9

    h-index

  3. Statistics in Medicine

    8 publications · 399 citations

    8

    h-index

  4. 8

    h-index

  5. 8

    h-index

1–5