At the heart of Bayesian optimisation is Bayes' theorem, named after Thomas Bayes (1701-1761). While Bayes never published his most famous accomplishment, his notes were collected and published posthumously by Richard Price in 1763. Bayes' theorem describes the correlation between two different events and is used to calculate the conditional probability of one event happening based on the condition that another event has occurred. If A and B are two events, the probably of A happening given the conditional event B is theorem is frequently applied in finance and medical tests. For example, to evaluate the probability a person has a disease given the result of a medical test (event B) and the accuracy of the test (event A). The term Bayesian optimisation is attributed to Jonas Mockus from his work on global optimisation during the 1970s and 1980s. Bayesian optimisation uses a sequential modelbased strategy for global optimisation. There are two key concepts: i) a surrogate function (statistical model) is introduced to estimate the posterior distribution of the objective function; and ii) an acquisition function is used to evaluate the domain of inputs and determine which point to sample next.