Pseudorandom Number Generation

  • George S. Fishman
Chapter
Part of the Springer Series in Operations Research book series (ORFE)

Abstract

Chapter 8 reveals that every algorithm that generates a sequence of i.i.d. random samples from a probability distribution as output requires a sequence of i.i.d. random samples from u(0, 1) as input. To meet this need, every discrete-event simulation programming language provides a pseudorandom number generator that produces a sequence of nonnegative integers Z 1, Z 2,... with integer upper bound M > Z i i and then uses U 1, U 2,..., where U i := Z i /M, as an approximation to an i.i.d. sequence from u(0, 1).

Keywords

Random Number Generator Discretization Error Pseudorandom Number Combine Generator Primitive Root 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 2001

Authors and Affiliations

  • George S. Fishman
    • 1
  1. 1.Department of Operations ResearchUniversity of North Carolina at Chapel HillChapel HillUSA

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