von Neumann’s comparison method for random sampling from the normal and other distributions

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.