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Abstract

Up to now, computers are only able to do deterministic tasks and they cannot generate true random numbers. To sample random numbers, they run deterministic sequences called pseudorandom number generators that produce a sequence of real numbers in [0, 1] that behaves like a sequence of independent random variables that are distributed uniformly on [0, 1]. Different families of pseudorandom number generators exist. It is important to use generators that have a large period, such as the Mersenne twister. In fact, running a Monte-Carlo algorithm to compute pathwise expectations may use intensively the generator. The convergence of the Monte-Carlo algorithm is degraded when the amount of pseudorandom numbers used is close or larger than the period.

Keywords

Order Scheme Infinitesimal Generator Standard Normal Variable High Order Scheme Switching Threshold 
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 International Publishing Switzerland 2015

Authors and Affiliations

  • Aurélien Alfonsi
    • 1
  1. 1.CERMICSEcole Nationale des Ponts et ChausséesChamps-sur-MarneFrance

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