The generation of random values corresponding to an underlying Gamma distribution is a key capability in many areas of knowledge, such as Probability and Statistics, Signal Processing, or Digital Communication, among others. Throughout history, different algorithms have been developed for the generation of such values and advances in computing have made them increasingly faster and more efficient from a computational point of view. These advances also allow the generation of higher-quality inputs (from the point of view of randomness and uniformity) for these algorithms that are easily tested by different statistical batteries such as NIST, Dieharder, or TestU01 among others. This article describes the existing algorithms for the generation of (independent and identically distributed—i.i.d.) Gamma distribution values as well as the theoretical and mathematical foundations that support their validity.

生成对应于底层 Gamma 分布的随机值是许多知识领域的关键能力，例如概率与统计、信号处理或数字通信等。纵观历史，已经开发了不同的算法来生成此类值，并且从计算的角度来看，计算的进步使它们变得越来越快和高效。这些进步还允许为这些算法生成更高质量的输入（从随机性和均匀性的角度来看），这些算法可以很容易地由不同的统计电池（如 NIST、Dieharder 或 TestU01 等）进行测试。本文介绍了现有的生成（独立同分布——iid