Bayesian Absorption Modeling

Bayesian Absorption Modeling is a statistical modeling framework that integrates prior knowledge and observed data through Bayesian inference to estimate absorption processes, distinguished by its probabilistic representation of uncertainty in absorption parameters and dynamics.

3

Publications

53

Citations

11

Authors

9

Institutions

Publications per year

2017–2026

3

Authors

11

  1. AA
    Andrew A. J. Schmitt

    Rensselaer Polytechnic Institute, United States

    1 publications · 19 citations

    1

    h-index

  2. NX
    Ning Xiang

    Rensselaer Polytechnic Institute, United States

    1 publications · 19 citations

    1

    h-index

  3. IA
    Ichiro Akai

    Osaka City University, Japan

    1 publications · 12 citations

    1

    h-index

  4. CJ
    Cheol-Ho Jeong

    Technical University of Denmark, Denmark

    1 publications · 22 citations

    1

    h-index

  5. FM
    Florian Muralter

    Ørsted (Denmark), Denmark

    1 publications · 22 citations

    1

    h-index

1–5 of 11

Venues

  1. The Journal of the Acoustical Society of America

    JASA · 2 publications · 41 citations

    2

    h-index

  2. Journal of the Physical Society of Japan

    1 publications · 12 citations

    1

    h-index

  3. Mechanical Systems and Signal Processing

    MSSP · 1 publications · 14 citations

  4. Inverse Problems

    1 publications · 13 citations

1–4