After each iteration, the new sample point is added to the set of observations, the posterior predictive distribution is reevaluated using the surrogate model, the mean and variance are recalculated, and the acquisition function is used to select the next input point. This process is repeated until the maximum number of learning cycles (or another convergence criterion) is reached. Bayesian optimisation has the advantage that it can be applied to complex search spaces with multiple categorical or conditional inputs. Several software libraries have been developed for performing Bayesian optimisation (Table ) and packages to increase its accessability such as Honegumi. 26