AI 中文总结
研究重新审视接受 - 补余法，指出其运行时间恒定可取代拒绝法，能高效生成多种分布随机变量，还存在适用于特定对数凹密度的该方法。
AI 中文摘要
我们重新审视随机变量生成中的接受 - 补余法，并展示它如何在许多例子中取代拒绝法。拒绝法执行时间呈几何分布，而接受 - 补余法运行时间恒定（确定性）且堪称‘一行代码实现’。我们展示该方法如何用于高效生成多种分布（如伽马和贝塔分布）的随机变量。此外，我们表明存在一种适用于所有具有已知众数位置且能对密度进行黑箱式访问的对数凹密度的接受 - 补余法。
英文摘要
We revisit the acceptance-complement method in random variate generation and show how it can replace the rejection method in many examples. While the rejection method has geometrically distributed execution times, the acceptance-complement method has a constant (deterministic) run time and qualifies as a ``one-liner''. We show how this method can be used to efficiently generate random variates from several distributions, such as the gamma and beta. In addition, we show that there is an acceptance-complement method that is valid for all log-concave densities with known location of the mode and black-box type access to the density.
