Bayesian Optimization Using Sequential Monte Carlo

  • Romain Benassi
  • Julien Bect
  • Emmanuel Vazquez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7219)


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.


Sequential Monte Carlo Expect Improvement Gaussian Process Model Reference Algorithm Bayesian Optimization 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Romain Benassi
    • 1
  • Julien Bect
    • 1
  • Emmanuel Vazquez
    • 1
  1. 1.SUPELECFrance

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