 Gaussian process regression posterior

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.