Adaptive Bayesian Learning

Adaptive Bayesian Learning is a Bayesian inference framework that iteratively refines probabilistic beliefs through sequential data acquisition, distinguished by its capacity to dynamically adjust prior distributions and model structures in response to evolving evidence.

118

Publications

7.1K

Citations

450

Authors

243

Institutions

Publications per year

2017–2026

51

Authors

450

  1. QH
    Qiang Huo

    Microsoft Research Asia (China), China

    4 publications · 270 citations

    4

    h-index

  2. CL
    Chin‐Hui Lee

    Georgia Institute of Technology, United States

    3 publications · 2.3K citations

    3

    h-index

  3. KM
    Kwang Mong Sim

    Gwangju Institute of Science and Technology, South Korea

    2 publications · 81 citations

    2

    h-index

  4. FG
    Fábio Gagliardi Cozman

    Universidade de São Paulo, Brazil

    2 publications · 42 citations

    2

    h-index

  5. SJ
    Sung Ju Hwang

    Korea Advanced Institute of Science and Technology, South Korea

    2 publications · 75 citations

    2

    h-index

1–5 of 450

Venues

  1. IEEE Transactions on Speech and Audio Processing

    5 publications · 2.3K citations

    5

    h-index

  2. arXiv (Cornell University)

    Repository · 3 publications · 88 citations

    4

    h-index

  3. Knowledge and Information Systems

    2 publications · 29 citations

    2

    h-index

  4. PRX Quantum

    2 publications · 81 citations

    2

    h-index

  5. SSRN Electronic Journal

    Repository · 2 publications · 116 citations

    2

    h-index

1–5