Bayesian Copula Models

Bayesian Copula Models is a statistical modeling framework that integrates copula functions with Bayesian inference to jointly model multivariate dependencies by separating the specification of marginal distributions from the dependence structure, while formally accounting for parameter uncertainty through posterior distributions.

121

Publications

5.4K

Citations

318

Authors

177

Institutions

Publications per year

2017–2026

72

Authors

318

  1. AS
    Antonia Sebastian

    Rice University, United States

    4 publications · 197 citations

    4

    h-index

  2. OM
    Oswaldo Morales‐Nápoles

    Delft University of Technology, Netherlands

    4 publications · 197 citations

    4

    h-index

  3. GH
    Guohe Huang

    University of Regina, Canada

    4 publications · 97 citations

    4

    h-index

  4. FK
    Faisal Khan

    Memorial University of Newfoundland, Canada

    3 publications · 158 citations

    3

    h-index

  5. YL
    Yongping Li

    University of Regina, Canada

    3 publications · 131 citations

    3

    h-index

1–5 of 318

Venues

  1. Journal of Hydrology

    6 publications · 228 citations

    6

    h-index

  2. Reliability Engineering & System Safety

    5 publications · 293 citations

    5

    h-index

  3. Statistics and Computing

    4 publications · 194 citations

    4

    h-index

  4. Water

    4 publications · 158 citations

    4

    h-index

  5. Computational Statistics & Data Analysis

    CSDA · 4 publications · 124 citations

    4

    h-index

1–5