Twister Generator of Random Normal Numbers by Box-Muller Model
- 1 N.E. Bauman Moscow State Technical University, Russia
- 2 University of Arkansas for Medical Sciences, United States
Copyright: © 2020 Aleksei F. Deon and Yulian A. Menyaev. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Twisting generators of the pseudorandom normal variables can use uniform random sequences as a basis. However, such technique could provide poor quality result in cases where the original sequences have insufficient uniformity or skipping of random values. This work offers a new approach for creating the random normal variables using the Box-Muller model as a basis together with the twisting generator of uniform planes. The simulation results confirm that the random variables obtained have a better approximation to normal Gaussian distribution. Moreover, combining this new approach with the tuning algorithm of basic twisting generation allows for a significantly increased the length of created sequences without using any additional random access memory of the computer.
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- Pseudorandom Number Generator
- Stochastic Sequences
- Congruential Numbers
- Twister Generator
- Normal Plane Generator