Gaussian process regression posterior
= Gaussian process regression posterior
{c}
For a zero-mean <Gaussian process> observed with independent <Gaussian noise>, the latent value at a new input is conditionally normal. Its mean is the kernel cross-covariance times the noisy kernel-matrix inverse times the observations, and its variance is the prior variance minus the corresponding quadratic form.