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Rejection-Inversion to Generate Variates from Monotone Discrete Distributions

By Wolfgang Hörmann and Gerhard Derflinger


For discrete distributions a variant of rejection from a continuous hat function is presented. The main advantage of the new method, called rejection-inversion, is that no extra uniform random number to decide between acceptance and rejection is required which means that the expected number of uniform variates required is halved. Using rejection-inversion and a squeeze, a simple universal method for a large class of monotone discrete distributions is developed. It can be used to generate variates from the tails of most standard discrete distributions. Rejection-inversion applied to the Zipf (or zeta) distribution results in algorithms that are short and simple and at least twice as fast as the fastest methods suggested in the literature. (author's abstract)Series: Preprint Series / Department of Applied Statistics and Data Processin

Topics: MSC 65C10, CCS G.3, random number generation / rejection method / Zipf distribution / tail of Poisson distribution / universal algorithm / T-concave
Publisher: Department of Statistics and Mathematics, Abt. f. Angewandte Statistik u. Datenverarbeitung, WU Vienna University of Economics and Business
Year: 1996
DOI identifier: 10.1145/235025.235029
OAI identifier:

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