Bayesian Source Attribution Models

Bayesian Source Attribution Models is a statistical modeling framework that employs Bayesian inference to probabilistically assign observed outcomes or data points to their most likely sources by updating prior beliefs with observed evidence in a coherent, uncertainty-aware manner.

13

Publications

1.8K

Citations

49

Authors

30

Institutions

Publications per year

2017–2026

6

Authors

49

  1. HC
    Ha‐Joon Chang

    University of Cambridge, United Kingdom

    3 publications · 54 citations

    3

    h-index

  2. MJ
    Mohsen Javdani

    Simon Fraser University, Canada

    3 publications · 54 citations

    3

    h-index

  3. MK
    Martyn Kirk

    Australian National University, Australia

    2 publications · 65 citations

    2

    h-index

  4. KG
    Kathryn Glass

    Australian National University, Australia

    2 publications · 65 citations

    2

    h-index

  5. DM
    Dmitri Matjushin

    Finnish Food Authority, Finland

    1 publications · 26 citations

    1

    h-index

1–5 of 49

Venues

  1. Risk Analysis

    3 publications · 94 citations

    3

    h-index

  2. SSRN Electronic Journal

    Repository · 2 publications · 35 citations

    2

    h-index

  3. BMC Infectious Diseases

    1 publications · 17 citations

    1

    h-index

  4. 1

    h-index

  5. Psychological Bulletin

    1 publications · 1.5K citations

    1

    h-index

1–5