Bayesian Risk Classification

Bayesian Risk Classification is a probabilistic classification framework that assigns risk categories to instances by updating prior beliefs with observed evidence through Bayes' theorem, distinguishing itself from classical risk classification methods through its explicit incorporation of uncertainty and prior knowledge into the decision process.

16

Publications

821

Citations

59

Authors

30

Institutions

Publications per year

2017–2026

3

Authors

59

  1. FS
    Fereshteh Sattari

    University of Alberta, Canada

    1 publications · 64 citations

    1

    h-index

  2. RJ
    Richard J. Povinelli

    Marquette University, United States

    1 publications · 64 citations

    1

    h-index

  3. CL
    Chia-Wen Liao

    China University of Technology, Taiwan

    1 publications · 30 citations

    1

    h-index

  4. LL
    Lianne Lefsrud

    University of Alberta, Canada

    1 publications · 64 citations

    1

    h-index

  5. PP
    Prachi Pradeep

    Research Triangle Park Foundation, United States

    1 publications · 64 citations

    1

    h-index

1–5 of 59

Venues

  1. PubMed

    Repository · 2 publications · 25 citations

    2

    h-index

  2. 1

    h-index

  3. Mathematical Problems in Engineering

    MPE · 1 publications · 14 citations

    1

    h-index

  4. Journal of Cheminformatics

    1 publications · 64 citations

    1

    h-index

  5. International Conference on Artificial Intelligence and Statistics

    Conference · 1 publications · 141 citations

    1

    h-index

1–5