The previous chapter used Gaussian processes (GP) as the surrogate model to approximate the underlying objective function. GP is a flexible framework that provides uncertainty estimates in the form of probability distributions over plausible functions across the entire domain. We could then resort to the closed-form posterior predictive distributions at proposed locations to obtain an educated guess on the potential observations.
However, it is not the only choice of surrogate model used in Bayesian ...
