利用有偏随机源生成离散均匀分布的有效方法

IF 0.7 4区数学 Q3 STATISTICS & PROBABILITY

Journal of Applied Probability Pub Date : 2023-03-07 DOI:10.1017/jpr.2022.111

Xiaoyu Lei

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引用次数: 0

摘要

摘要提出了一种利用p素数的有偏随机源在p元素集合上生成离散均匀分布的有效算法。该算法推广了Von Neumann方法，提高了Dijkstra方法的计算效率。此外，将该算法推广到在任意有限集上基于整数的质因数分解生成离散均匀分布。本文算法的平均运行时间总体上是次线性的:$\operatorname{O}\!(n/\log n)$。

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An efficient method for generating a discrete uniform distribution using a biased random source

Abstract We present an efficient algorithm to generate a discrete uniform distribution on a set of p elements using a biased random source for p prime. The algorithm generalizes Von Neumann’s method and improves the computational efficiency of Dijkstra’s method. In addition, the algorithm is extended to generate a discrete uniform distribution on any finite set based on the prime factorization of integers. The average running time of the proposed algorithm is overall sublinear: $\operatorname{O}\!(n/\log n)$ .

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来源期刊

Journal of Applied Probability 数学-统计学与概率论

CiteScore

1.50

自引率

10.00%

发文量

审稿时长

6-12 weeks

期刊介绍： Journal of Applied Probability is the oldest journal devoted to the publication of research in the field of applied probability. It is an international journal published by the Applied Probability Trust, and it serves as a companion publication to the Advances in Applied Probability. Its wide audience includes leading researchers across the entire spectrum of applied probability, including biosciences applications, operations research, telecommunications, computer science, engineering, epidemiology, financial mathematics, the physical and social sciences, and any field where stochastic modeling is used. A submission to Applied Probability represents a submission that may, at the Editor-in-Chief’s discretion, appear in either the Journal of Applied Probability or the Advances in Applied Probability. Typically, shorter papers appear in the Journal, with longer contributions appearing in the Advances.