Bayesian Confidence Intervals

Bayesian Confidence Intervals is a statistical interval estimation method that quantifies the uncertainty of a parameter by specifying a range of values within which the parameter lies with a certain probability, derived from the posterior distribution in a Bayesian framework, thereby distinguishing it from frequentist confidence intervals by incorporating prior knowledge and yielding direct probabilistic interpretations about the parameter.

427

Publications

55.5K

Citations

1K

Authors

535

Institutions

Publications per year

2017–2026

97

Authors

1K

  1. SN
    Sa‐Aat Niwitpong

    King Mongkut's University of Technology North Bangkok, Thailand

    12 publications · 282 citations

    11

    h-index

  2. SN
    Suparat Niwitpong

    King Mongkut's University of Technology North Bangkok, Thailand

    11 publications · 261 citations

    11

    h-index

  3. PM
    Patcharee Maneerat

    King Mongkut's University of Technology North Bangkok, Thailand

    6 publications · 161 citations

    6

    h-index

  4. GW
    Grace Wahba

    University of Wisconsin–Madison, United States

    6 publications · 997 citations

    6

    h-index

  5. EW
    Eric‐Jan Wagenmakers

    University of Amsterdam, Netherlands

    5 publications · 1.8K citations

    5

    h-index

1–5 of 1K

Venues

  1. 17

    h-index

  2. Journal of the American Statistical Association

    21 publications · 1.3K citations

    16

    h-index

  3. Journal of Statistical Computation and Simulation

    JSCS · 12 publications · 393 citations

    11

    h-index

  4. Statistics in Medicine

    11 publications · 484 citations

    11

    h-index

  5. IEEE Transactions on Reliability

    8 publications · 180 citations

    8

    h-index

1–5