Creating and Training the Gaussian Process Regression Model Creating the Gaussian process regression model is simultaneously simple and complicated. ... This is useful if a trained model is going to ...

Researchers explored the decision-making process of Gaussian process (GP) models, focusing on loss landscapes and ...

We model the joint posterior of the derivatives as a Gaussian process over function space, imposing the spatial covariancestructure on the risk factors. Monte Carlo simulation is then used to simulate ...

METHODS We present a Semi-Artificial Model of Population, which aims to bridge demographic micro-simulation and agent-based traditions. We then utilise a Gaussian process emulator – a statistical ...