Fig. 2.

Testing Bayesian optimization by finding the maximum of a two-dimensional function. a The acquisition function decides the next input to test and the output is used to refine the predictive model. Iterations 5, 10, 15 and 20 of this process are shown. b With increasing rounds of iteration, the predictive model grows more confident of the location of the global maximum and the distance between tested inputs decreases with each iteration. c The algorithm evaluated 9 points before finding the location of the maximum. Subsequent iterations tuned this approximation toward the true optimum. The algorithm evaluated 12 points before finding the maximum. The order in which each point is evaluated is shown on the graph