Adaptive Bayesian Inference

Adaptive Bayesian Inference is a probabilistic inference framework that dynamically updates belief distributions over latent variables or model parameters in response to sequential evidence, distinguishing itself from static Bayesian methods by incorporating feedback-driven adaptation of the inference process to optimize diagnostic accuracy in real-time.

253

Publications

14.3K

Citations

844

Authors

427

Institutions

Publications per year

2017–2026

78

Authors

844

  1. PF
    Peter F. Thall

    The University of Texas MD Anderson Cancer Center, United States

    6 publications · 819 citations

    6

    h-index

  2. ZL
    Zhong‐Lin Lu

    University of Southern California, United States

    6 publications · 339 citations

    6

    h-index

  3. SB
    Scott Berry

    Berry & Associates (United States), United States

    4 publications · 198 citations

    4

    h-index

  4. GY
    Guosheng Yin

    University of Hong Kong, Hong Kong SAR China

    3 publications · 238 citations

    3

    h-index

  5. MK
    Mojtaba Kordestani

    University of Windsor, Canada

    3 publications · 189 citations

    3

    h-index

1–5 of 844

Venues

  1. arXiv (Cornell University)

    Repository · 9 publications · 350 citations

    9

    h-index

  2. Biometrics

    7 publications · 943 citations

    7

    h-index

  3. Statistics in Medicine

    7 publications · 407 citations

    7

    h-index

  4. The Annals of Statistics

    6 publications · 475 citations

    6

    h-index

  5. 5

    h-index

1–5