Gaussian-process marginal likelihood

For Gaussian-process latent values with mean vector $m$ and covariance matrix $K$, observed with independent Gaussian noise covariance $E$, integrating out the latent values gives the marginal distribution $N(m,K+E)$. Its density as a function of the mean and kernel parameters is the Gaussian-process marginal likelihood.