Stochastic Process Simulation

Stochastic Process Simulation is a computational modeling approach that belongs to the class of process simulation techniques and is distinguished by its incorporation of random variables and probabilistic transitions to represent uncertainty and variability in dynamic systems over time.

656

Publications

37.3K

Citations

1.6K

Authors

715

Institutions

Publications per year

2017–2026

130

Authors

1.6K

  1. PW
    Peter W. Glynn

    Stanford University, United States

    9 publications · 668 citations

    9

    h-index

  2. WD
    W. David Kelton

    University of Cincinnati, United States

    8 publications · 954 citations

    8

    h-index

  3. JR
    James R. Wilson

    North Carolina State University, United States

    7 publications · 197 citations

    7

    h-index

  4. DL
    Donald L. Iglehart

    Stanford University, United States

    6 publications · 584 citations

    6

    h-index

  5. DK
    Demetris Koutsoyiannis

    National Technical University of Athens, Greece

    6 publications · 225 citations

    6

    h-index

1–5 of 1.6K

Venues

  1. Journal of Engineering Mechanics

    11 publications · 1.7K citations

    11

    h-index

  2. Operations Research

    10 publications · 663 citations

    10

    h-index

  3. Probabilistic Engineering Mechanics

    9 publications · 494 citations

    9

    h-index

  4. Computers & Chemical Engineering

    9 publications · 279 citations

    9

    h-index

  5. Technometrics

    8 publications · 1.2K citations

    8

    h-index

1–5