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Erschienen in:
2012 | OriginalPaper | Buchkapitel
Bayesian Optimization Using Sequential Monte Carlo
verfasst von : Romain Benassi, Julien Bect, Emmanuel Vazquez
Erschienen in: Learning and Intelligent Optimization
Verlag: Springer Berlin Heidelberg
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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.