从无高斯成分的莱维过程采样的弗格森-克拉斯算法的通用近似值

arXiv - STAT - Computation Pub Date : 2024-07-01 DOI:arxiv-2407.01483

Dawid Bernaciak, Jim E. Griffin

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引用次数: 0

摘要

我们提出了一种通用的近似弗格森-克拉斯算法，用于从没有高斯成分的 L\'evy 过程中生成样本。结果表明，所提出的方法比标准的弗格森-克拉斯算法快 1000 多倍，而且精度没有明显下降。正如示例部分所展示的，这种方法可以为计算高效、可扩展的贝叶斯非参数模型开辟一条途径，这些模型超越了共轭假设。

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A General Purpose Approximation to the Ferguson-Klass Algorithm for Sampling from Lévy Processes Without Gaussian Components

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.

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arXiv - STAT - Computation

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