This example illustrates GPC on XOR data. Compared are a stationary, isotropic kernel (RBF) and a non-stationary kernel

This example illustrates that GPR with a sum-kernel including a WhiteKernel can estimate the noise level of data. An illustration

Both kernel ridge regression (KRR) and Gaussian process regression (GPR) learn a target function by employing internally the ?kernel

This example illustrates the prior and posterior of a GPR with different kernels. Mean, standard deviation, and 10

A two-dimensional classification example showing iso-probability lines for the predicted probabilities.

This example illustrates the predicted probability of GPC for an RBF kernel with different choices of the hyperparameters

This example is based on Section 5.4.3 of ?Gaussian Processes for Machine Learning? [RW2006]. It illustrates an example of complex kernel

A simple one-dimensional regression example computed in two different ways: A noise-free case

This example illustrates the predicted probability of GPC for an isotropic and anisotropic RBF kernel on a two-dimensional version for the