Bayesian optimization using sequential Monte Carlo - CentraleSupélec Access content directly
Conference Papers Year : 2012

Bayesian optimization using sequential Monte Carlo


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.
Fichier principal
Vignette du fichier
article_lion6.pdf (93.87 Ko) Télécharger le fichier
Origin Files produced by the author(s)

Dates and versions

hal-00717195 , version 1 (12-07-2012)



Romain Benassi, Julien Bect, Emmanuel Vazquez. Bayesian optimization using sequential Monte Carlo. 6th International Conference on Learning and Intelligent Optimization (LION6), Jan 2012, Paris, France. pp.339-342, ⟨10.1007/978-3-642-34413-8_24⟩. ⟨hal-00717195⟩
225 View
242 Download



Gmail Mastodon Facebook X LinkedIn More