Stochastic Modeling

Stochastic modeling is a quantitative analytical method that employs random variables and probability distributions to characterize uncertainty and derive distributions of possible outcomes.

46.9K

Publications

2.9M

Citations

69.4K

Authors

9K

Institutions

Publications per year

2017–2026

10.8K

Authors

69.4K

  1. ZW
    Zidong Wang

    Brunel University of London, United Kingdom

    102 publications · 10.7K citations

    59

    h-index

  2. XM
    Xuerong Mao

    University of Strathclyde, United Kingdom

    89 publications · 8.2K citations

    43

    h-index

  3. NB
    N. Balakrishnan

    McMaster University, Canada

    102 publications · 4.9K citations

    37

    h-index

  4. GO
    Gareth O. Roberts

    University of Warwick, United Kingdom

    55 publications · 10.6K citations

    36

    h-index

  5. PS
    Peng Shi

    The University of Adelaide, Australia

    51 publications · 4.9K citations

    35

    h-index

1–5 of 69.4K

Venues

  1. Journal of the American Statistical Association

    639 publications · 118.7K citations

    146

    h-index

  2. IEEE Transactions on Power Systems

    466 publications · 46.3K citations

    118

    h-index

  3. IEEE Transactions on Automatic Control

    568 publications · 56.7K citations

    115

    h-index

  4. Technometrics

    432 publications · 71.6K citations

    113

    h-index

  5. Water Resources Research

    354 publications · 31.3K citations

    90

    h-index

1–5

See also

Stochastic Modeling

Core Methods

Applications

Variants

Evaluation

Limitations