Uncertain Data Modeling

Uncertain Data Modeling is a methodological framework within Gaussian Process Modeling that formally characterizes and propagates input and output uncertainties through probabilistic inference to yield robust predictive distributions.

720

Publications

44.1K

Citations

1.8K

Authors

768

Institutions

Publications per year

2017–2026

243

Authors

1.8K

  1. RC
    Reynold Cheng

    University of Hong Kong, Hong Kong SAR China

    17 publications · 1.7K citations

    15

    h-index

  2. LC
    Lei Chen

    Hong Kong University of Science and Technology, Hong Kong SAR China

    14 publications · 775 citations

    12

    h-index

  3. XL
    Xuemin Lin

    UNSW Sydney, Australia

    11 publications · 1.1K citations

    11

    h-index

  4. CK
    Carson K. Leung

    University of Manitoba, Canada

    9 publications · 332 citations

    9

    h-index

  5. JP
    Jian Pei

    Simon Fraser University, Canada

    9 publications · 1.1K citations

    9

    h-index

1–5 of 1.8K

Venues

  1. Proceedings of the VLDB Endowment

    PVLDB · 19 publications · 1.1K citations

    17

    h-index

  2. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 18 publications · 1.7K citations

    17

    h-index

  3. arXiv (Cornell University)

    Repository · 22 publications · 777 citations

    14

    h-index

  4. Information Sciences

    14 publications · 744 citations

    13

    h-index

  5. IEEE Transactions on Fuzzy Systems

    TFS · 10 publications · 581 citations

    10

    h-index

1–5