no code implementations • 3 Jun 2021 • Thomas Pinder, Kathryn Turnbull, Christopher Nemeth, David Leslie
We derive a Matern Gaussian process (GP) on the vertices of a hypergraph.
1 code implementation • 25 Sep 2020 • Thomas Pinder, Christopher Nemeth, David Leslie
We show how to use Stein variational gradient descent (SVGD) to carry out inference in Gaussian process (GP) models with non-Gaussian likelihoods and large data volumes.
3 code implementations • 21 Dec 2018 • Jamie Fairbrother, Christopher Nemeth, Maxime Rischard, Johanni Brea, Thomas Pinder
Gaussian processes are a class of flexible nonparametric Bayesian tools that are widely used across the sciences, and in industry, to model complex data sources.