Probabilistic Regression

Probabilistic Regression is a regression framework that, as a member of the class of probabilistic machine learning methods, models the conditional distribution of the response variable given the inputs to quantify predictive uncertainty, distinguishing itself from deterministic regression by providing a full probability distribution over possible outputs rather than a single point estimate.

133

Publications

8.9K

Citations

372

Authors

221

Institutions

Publications per year

2017–2026

58

Authors

372

  1. ZS
    Zhihuan Song

    State Key Laboratory of Industrial Control Technology

    5 publications · 499 citations

    5

    h-index

  2. ZG
    Zhiqiang Ge

    State Key Laboratory of Industrial Control Technology

    5 publications · 473 citations

    5

    h-index

  3. XY
    Xiaofeng Yuan

    Central South University, China

    4 publications · 332 citations

    4

    h-index

  4. YW
    Yalin Wang

    Central South University, China

    4 publications · 332 citations

    4

    h-index

  5. JZ
    Junhua Zheng

    Zhejiang University of Science and Technology, China

    2 publications · 163 citations

    2

    h-index

1–5 of 372

Venues

  1. arXiv (Cornell University)

    Repository · 6 publications · 303 citations

    6

    h-index

  2. IEEE Transactions on Industrial Informatics

    5 publications · 265 citations

    5

    h-index

  3. Journal of the American Statistical Association

    3 publications · 1.1K citations

    3

    h-index

  4. IEEE Access

    3 publications · 130 citations

    3

    h-index

  5. Structural Health Monitoring

    SHM · 2 publications · 125 citations

    2

    h-index

1–5