Ziggurat algorithm

Algorithm for pseudo-random number sampling / From Wikipedia, the free encyclopedia

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The ziggurat algorithm is an algorithm for pseudo-random number sampling. Belonging to the class of rejection sampling algorithms, it relies on an underlying source of uniformly-distributed random numbers, typically from a pseudo-random number generator, as well as precomputed tables. The algorithm is used to generate values from a monotonically decreasing probability distribution. It can also be applied to symmetric unimodal distributions, such as the normal distribution, by choosing a value from one half of the distribution and then randomly choosing which half the value is considered to have been drawn from. It was developed by George Marsaglia and others in the 1960s.

A typical value produced by the algorithm only requires the generation of one random floating-point value and one random table index, followed by one table lookup, one multiply operation and one comparison. Sometimes (2.5% of the time, in the case of a normal or exponential distribution when using typical table sizes)[citation needed] more computations are required. Nevertheless, the algorithm is computationally much faster[citation needed] than the two most commonly used methods of generating normally distributed random numbers, the Marsaglia polar method and the Box–Muller transform, which require at least one logarithm and one square root calculation for each pair of generated values. However, since the ziggurat algorithm is more complex to implement it is best used when large quantities of random numbers are required.

The term ziggurat algorithm dates from Marsaglia's paper with Wai Wan Tsang in 2000; it is so named because it is conceptually based on covering the probability distribution with rectangular segments stacked in decreasing order of size, resulting in a figure that resembles a ziggurat.

The Ziggurat algorithm used to generate sample values with a normal distribution. (Only positive values are shown for simplicity.) The pink dots are initially uniform-distributed random numbers. The desired distribution function is first segmented into equal areas "A". One layer i is selected at random by the uniform source at the left. Then a random value from the top source is multiplied by the width of the chosen layer, and the result is x tested to see which region of the slice it falls into with 3 possible outcomes: 1) (left, solid black region) the sample clearly under the curve and is passed directly to output, 2) (right, vertically striped region) the sample value may lie under the curve, and must be tested further. In that case, a random y value within the chosen layer is generated and compared to f(x). If less, the point is under the curve and the value x is output. If not, (the third case), the chosen point x is rejected and the algorithm is restarted from the beginning.