Variance-based Decomposition

Variance-Based Decomposition is a systematic method in uncertainty quantification that decomposes the variance of a model output into contributions from individual input variables and their interactions, thereby enabling the identification of the most influential sources of uncertainty.

192

Publications

9.8K

Citations

496

Authors

292

Institutions

Publications per year

2017–2026

72

Authors

496

  1. DS
    Dan Segal

    University of Toronto, Canada

    5 publications · 374 citations

    5

    h-index

  2. JL
    Jeffrey L. Callen

    University of Toronto, Canada

    5 publications · 374 citations

    5

    h-index

  3. ZL
    Zhenzhou Lü

    Northwestern Polytechnical University, China

    4 publications · 107 citations

    4

    h-index

  4. AS
    Andrea Saltelli

    Joint Research Centre, Italy

    3 publications · 544 citations

    3

    h-index

  5. LA
    Laura Almasy

    Texas Biomedical Research Institute, United States

    3 publications · 138 citations

    3

    h-index

1–5 of 496

Venues

  1. SSRN Electronic Journal

    Repository · 10 publications · 325 citations

    10

    h-index

  2. arXiv (Cornell University)

    Repository · 5 publications · 206 citations

    5

    h-index

  3. Strategic Management Journal

    SMJ · 4 publications · 672 citations

    4

    h-index

  4. Reliability Engineering & System Safety

    3 publications · 142 citations

    3

    h-index

  5. Journal of Climate

    2 publications · 43 citations

    2

    h-index

1–5