Surrogate models are used to estimate the value and uncertainty in the unseen portion of the objective function. Consider the situation where there are two initial known points on a 1D optimisation surface. We can guess the path that the objective function takes between the two points. By making many such guesses, we will build up a distribution of paths. The distribution will be narrower in the regions around the two known points and broadest roughly halfway between the points. This distribution of target values is similar to known probability distributions, such as a Gaussian process. Since we can now estimate the distribution of the target property, we can calculate the mean and variance across the domain and use it in the acquisition function. We now provide a summary of the two most common surrogate model algorithms used in Bayesian optimisation.