Bernaciak, Dawid; Griffin, Jim E; (2025) A General Purpose Approximation to the Ferguson-Klass Algorithm for Sampling from Lévy Processes Without Gaussian Components. Journal of Computational and Graphical Statistics (JCGS) (In press).

Text General_Purpose_Approximation_to_the_Ferguson_Klass_Algorithm.pdf - Accepted Version Access restricted to UCL open access staff until 3 June 2026. Download (565kB)

Abstract

We propose a general-purpose method for generating samples from L´evy processes without Gaussian components. It uses a multi-part approximations of the jump intensity on a grid and applies the Ferguson-Klass algorithm. We consider how the choice of grid affects the approximation error and propose adaptive selection methods that lead to negligible approximation error. The proposed method is shown to be orders of magnitude faster than the original Ferguson-Klass algorithm and competitive with tailored methods. The method opens 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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