Bayesian optimization using sequential Monte Carlo
Optimization and Control
2011-11-22 v1 Machine Learning
Computation
Abstract
We consider the problem of optimizing a real-valued continuous function using a Bayesian approach, where the evaluations of are chosen sequentially by combining prior information about , 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.
Cite
@article{arxiv.1111.4802,
title = {Bayesian optimization using sequential Monte Carlo},
author = {Romain Benassi and Julien Bect and Emmanuel Vazquez},
journal= {arXiv preprint arXiv:1111.4802},
year = {2011}
}