In this work, we propose a novel inference method for DGPs for computer model emulation. By stochastically imputing the latent layers, our approach transforms a DGP into a linked GP: a novel emulator developed for systems of linked computer models. This transformation permits an efficient DGP ...
In the previous session on Gaussian processes, we introduced the Gaussian process model and the covariance function. In this session we are going to address two challenges of the Gaussian process. Firstly, we look at the computational tractability and secondly we look at extending the nature of t...
On the processing side, the Python Application Programming Interface (API) package ee provides functions that allow to extract any available information layer over a specific area of interest (AOI) and process the resulting datasets very efficiently, thus enabling studies at any place on Earth and ...
Fig. 1. Gaussian Process Regression kernel parameters mapping. In every regression-based ML problem, the goal is to calculate the function that closely fits the input dataset. In addition, not only a close-fitting function is desirable, but the certainty of predictions must also be calculated. ...