Random number generators: good ones are hard to find
Communications of the ACM
The art of computer programming, volume 2 (3rd ed.): seminumerical algorithms
The art of computer programming, volume 2 (3rd ed.): seminumerical algorithms
Discrete-event simulation
Simulation Modeling and Analysis
Simulation Modeling and Analysis
Discrete-Event Simulation: A First Course
Discrete-Event Simulation: A First Course
A plug-in-based architecture for random number generation in simulation systems
Proceedings of the 40th Conference on Winter Simulation
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One of the most popular methods of generating "random" numbers for use in a computer program employs a Lehmer random number generator. If constructed properly, these generators will yield streams of numbers that will appear random but can also be easily replicated. However, Marsaglia (1968) discovered a potential flaw in these random number generators, namely, if consecutive n-tuples of the generated numbers are plotted in n-dimensional space, the points may fall into a small number of hyperplanes. In this paper, we compare the theoretical distribution of the distances to the actual distances produced by a specific random number generator in order to devise a statistical test of randomness for the validity of the random number generator.