Probabilistic Mixture Modeling

Probabilistic Mixture Modeling is a statistical modeling framework that characterizes the combined effects of multiple agents in a mixture by representing dose-response relationships through probability distributions to quantify uncertainty and variability in component interactions.

148

Publications

12.1K

Citations

460

Authors

251

Institutions

Publications per year

2017–2026

39

Authors

460

  1. ZS
    Zhihuan Song

    State Key Laboratory of Industrial Control Technology

    4 publications · 210 citations

    4

    h-index

  2. ZG
    Zhiqiang Ge

    State Key Laboratory of Industrial Control Technology

    3 publications · 185 citations

    3

    h-index

  3. DZ
    Djemel Ziou

    Université de Sherbrooke, Canada

    3 publications · 116 citations

    3

    h-index

  4. EL
    Ezequiel López‐Rubio

    Universidad de Málaga, Spain

    3 publications · 73 citations

    3

    h-index

  5. CZ
    ChengXiang Zhai

    University of Illinois Urbana-Champaign, United States

    3 publications · 515 citations

    3

    h-index

1–5 of 460

Venues

  1. arXiv (Cornell University)

    Repository · 6 publications · 174 citations

    6

    h-index

  2. Biometrics

    4 publications · 99 citations

    4

    h-index

  3. PubMed

    Repository · 4 publications · 129 citations

    4

    h-index

  4. Statistics in Medicine

    3 publications · 370 citations

    3

    h-index

  5. Forensic Science International Genetics

    FSI · 2 publications · 27 citations

    2

    h-index

1–5