Beyond Intuition, a Framework for Applying GPs to Real-World Data

1 code implementation • 6 Jul 2023 • Kenza Tazi, Jihao Andreas Lin, Ross Viljoen, Alex Gardner, ST John, Hong Ge, Richard E. Turner

Gaussian Processes (GPs) offer an attractive method for regression over small, structured and correlated datasets.

Gaussian Processes regression

Pyrocast: a Machine Learning Pipeline to Forecast Pyrocumulonimbus (PyroCb) Clouds

no code implementations • 22 Nov 2022 • Kenza Tazi, Emiliano Díaz Salas-Porras, Ashwin Braude, Daniel Okoh, Kara D. Lamb, Duncan Watson-Parris, Paula Harder, Nis Meinert

The pipeline's first two components, a pyroCb database and a pyroCb forecast model, are presented.

Identifying the Causes of Pyrocumulonimbus (PyroCb)

no code implementations • 16 Nov 2022 • Emiliano Díaz Salas-Porras, Kenza Tazi, Ashwin Braude, Daniel Okoh, Kara D. Lamb, Duncan Watson-Parris, Paula Harder, Nis Meinert

A first causal discovery analysis from observational data of pyroCb (storm clouds generated from extreme wildfires) is presented.

Causal Discovery

Kernel Learning for Explainable Climate Science

1 code implementation • 11 Sep 2022 • Vidhi Lalchand, Kenza Tazi, Talay M. Cheema, Richard E. Turner, Scott Hosking

We account for the spatial variation in precipitation with a non-stationary Gibbs kernel parameterised with an input dependent lengthscale.

Gaussian Processes