Random Number Generation

Random Number Generation is a computational and physical process that produces sequences of numbers or symbols whose statistical properties realize or approximate those of ideal randomness, typically by employing deterministic pseudorandom algorithms or nondeterministic entropy sources for use in simulation, cryptography, and statistical sampling.

2.8K

Publications

173.5K

Citations

5.8K

Authors

1.7K

Institutions

Publications per year

2017–2026

842

Authors

5.8K

  1. PL
    Pierre L’Ecuyer

    Université de Montréal, Canada

    51 publications · 4.8K citations

    34

    h-index

  2. HN
    Harald Niederreiter

    Austrian Academy of Sciences, Austria

    31 publications · 4K citations

    20

    h-index

  3. YW
    Yuncai Wang

    Taiyuan University of Technology, China

    25 publications · 1.1K citations

    19

    h-index

  4. GM
    George Marsaglia

    Florida State University, United States

    21 publications · 3.3K citations

    18

    h-index

  5. PL
    Pu Li

    Taiyuan University of Technology, China

    20 publications · 850 citations

    17

    h-index

1–5 of 5.8K

Venues

  1. Optics Express

    44 publications · 3.5K citations

    29

    h-index

  2. Mathematics of Computation

    MCOM · 56 publications · 2.5K citations

    27

    h-index

  3. 25

    h-index

  4. IEEE Transactions on Information Theory

    36 publications · 1.8K citations

    23

    h-index

  5. IEEE Access

    33 publications · 1.2K citations

    23

    h-index

1–5