Abstract
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.
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Benassi, R., Bect, J., Vazquez, E. (2012). Bayesian Optimization Using Sequential Monte Carlo. In: Hamadi, Y., Schoenauer, M. (eds) Learning and Intelligent Optimization. LION 2012. Lecture Notes in Computer Science, vol 7219. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34413-8_24
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DOI: https://doi.org/10.1007/978-3-642-34413-8_24
Publisher Name: Springer, Berlin, Heidelberg
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