Bayesian Risk Modeling

Bayesian Risk Modeling is a risk modeling framework that employs Bayesian inference to update the probability estimates of risky events by integrating prior knowledge with observed data, thereby distinguishing itself through its coherent treatment of uncertainty via subjective priors and recursive learning.

287

Publications

13.9K

Citations

853

Authors

414

Institutions

Publications per year

2017–2026

114

Authors

853

  1. MN
    Martin Neil

    Queen Mary University of London, United Kingdom

    7 publications · 398 citations

    7

    h-index

  2. NF
    Norman Fenton

    Queen Mary University of London, United Kingdom

    6 publications · 360 citations

    6

    h-index

  3. TA
    Terje Aven

    University of Stavanger, Norway

    5 publications · 114 citations

    5

    h-index

  4. ZY
    Zaili Yang

    Liverpool John Moores University, United Kingdom

    5 publications · 571 citations

    5

    h-index

  5. CG
    C. Guedes Soares

    University of Lisbon, Portugal

    5 publications · 217 citations

    5

    h-index

1–5 of 853

Venues

  1. Reliability Engineering & System Safety

    25 publications · 2.4K citations

    21

    h-index

  2. Risk Analysis

    17 publications · 787 citations

    13

    h-index

  3. Safety Science

    8 publications · 796 citations

    8

    h-index

  4. Process Safety and Environmental Protection

    5 publications · 265 citations

    5

    h-index

  5. Ocean & Coastal Management

    5 publications · 238 citations

    5

    h-index

1–5