论文标题
改进的标准正态分布的精确采样算法
An Improved Exact Sampling Algorithm for the Standard Normal Distribution
论文作者
论文摘要
2016年，卡尼（Karney）提出了标准正态分布的精确采样算法。在本文中，我们研究了该算法在随机偏差模型下的计算复杂性。具体而言，Karney的算法需要在整个范围内独立和均匀的随机偏差访问（0,1）。我们对本算法使用的预期均匀偏差数量进行估计，直到输出样品值，并提出改进的算法，并具有较低的均匀消耗。实验结果还表明，我们的改进算法的性能比Karney的算法更好。
In 2016, Karney proposed an exact sampling algorithm for the standard normal distribution. In this paper, we study the computational complexity of this algorithm under the random deviate model. Specifically, Karney's algorithm requires the access to an infinite sequence of independently and uniformly random deviates over the range (0,1). We give an estimate of the expected number of uniform deviates used by this algorithm until outputting a sample value, and present an improved algorithm with lower uniform deviate consumption. The experimental results also shows that our improved algorithm has better performance than Karney's algorithm.
