A General Purpose Approximation to the Ferguson-Klass Algorithm for Sampling from L\'evy Processes Without Gaussian Components
Computation
2025-05-14 v2 Applications
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.
Cite
@article{arxiv.2407.01483,
title = {A General Purpose Approximation to the Ferguson-Klass Algorithm for Sampling from L\'evy Processes Without Gaussian Components},
author = {Dawid Bernaciak and Jim E. Griffin},
journal= {arXiv preprint arXiv:2407.01483},
year = {2025}
}