  We consider the problem of optimizing a real-valued continuous function $f$
using a Bayesian approach, where the evaluations of $f$ are chosen sequentially
by combining prior information about $f$, which is described by a random
process model, and past evaluation results. The main difficulty with this
approach is to be able to compute the posterior distributions of quantities of
interest which are used to choose evaluation points. In this article, we decide
to use a Sequential Monte Carlo (SMC) approach.

Optimization and Control;Machine Learning;Computation