We propose a general-purpose approximation to the Ferguson-Klass algorithm for generating samples from L\'evy processes without Gaussian components. We show that the proposed method is more than 1000 times faster than the standard Ferguson-Klass algorithm without a significant loss of precision. This method can open an avenue for computationally efficient and scalable Bayesian nonparametric models which go beyond conjugacy assumptions, as demonstrated in the examples section.

翻译：本文提出了一种用于生成无高斯分量Lévy过程样本的Ferguson-Klass算法的通用近似方法。研究表明，该方法在精度无显著损失的前提下，其计算速度比标准Ferguson-Klass算法快1000倍以上。如示例部分所示，该方法可为超越共轭假设的计算高效、可扩展的贝叶斯非参数模型开辟新途径。