Different probabilistic models can be used in Bayesian optimization algorithm, for instance Gaussian Process (GP) , random forests , or Student-t processes . Gaussian processes are the default choice because of their flexibility and tractability. A GP is a stochastic process for which any finite combination of random variables follow a multivariate Gaussian distribution and its properties are determined by a mean function and a positive definite covariance function. The properties of the Gaussian distribution allow us to compute marginals and conditionals in a closed form. .