Pseudorandom Number Generators

  • Christiane Lemieux
Part of the Springer Series in Statistics book series (SSS)

As seen in the previous chapters, the use of the Monte Carlo method relies on the availability of uniform random numbers in order to perform random sampling. Although theoretical results for this method are based on the assumption that truly uniform random numbers are used, in practice, and as mentioned in Sect. 1.4, pseudorandom numbers are used. That is, we use sequences of numbers that look like they are random but that are in fact produced by a deterministic algorithm called a pseudorandom number generator (PRNG).


Maximal Period Nonlinear Generator Pseudorandom Number Generator Linear Feedback Shift Register Uniform Random Number 
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-Verlag New York 2009

Authors and Affiliations

  1. 1.University of WaterlooDept. Statistics & Actuarial ScienceWaterlooCanada

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