Is it possible to perform an approximated fully Bayesian (1) selection of hyper-parameters (e.g. covariance scale) with the GPML code, instead of maximizing the marginal likelihood (2) ? I think using ...
I am trying to optimize the hyperparameters for a Gaussian process. As a starter I choose the squared exponential function for covariance where iI have to optimize 3 parameters $\sigma_f$, $\sigma_n$ ...