Report Number: CS-TR-72-254
Institution: Stanford University, Department of Computer Science
Title: Von Neumann's comparison method for random sampling from the
normal and other distributions.
Author: Forsythe, George E.
Date: January 1972
Abstract: The author presents a generalization he worked out in 1950 of
von Neumann's method of generating random samples from the
exponential distribution by comparisons of uniform random
numbers on (0,1). It is shown how to generate samples from
any distribution whose probability density function is
piecewise both absolutely continuous and monotonic on
($-\infty$,$\infty$). A special case delivers normal deviates
at an average cost of only 4.036 uniform deviates each. This
seems more efficient than the Center-Tail method of Dieter
and Ahrens, which uses a related, but different, method of
generalizing the von Neumann idea to the normal distribution.