GP regression also permits an analytic formulation of the marginal likelihood, p(y|X) = f p(y|X, f )p(f ), which can be used for model training (X are the training inputs).
we can derive the gradient of the (log) marginal likelihood with respect to the model hyperparameters (i.e., a, σ n , σ s etc.)