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arXiv · 2407.01483

A General Purpose Approximation to the Ferguson-Klass Algorithm for Sampling from L\'evy Processes Without Gaussian Components

Dawid Bernaciak·Jim E. Griffin
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Abstract

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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Dawid Bernaciak, Jim E. Griffin. 2024-07-01. A General Purpose Approximation to the Ferguson-Klass Algorithm for Sampling from L\'evy Processes Without Gaussian Components. https://arxiv.org/abs/2407.01483

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