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Gaussian processes for computer experiments

Author

Listed:
  • François Bachoc

    (LPMA - Laboratoire de Probabilités et Modèles Aléatoires - UPMC - Université Pierre et Marie Curie - Paris 6 - UPD7 - Université Paris Diderot - Paris 7 - CNRS - Centre National de la Recherche Scientifique)

  • Emile Contal

    (CMLA - Centre de Mathématiques et de Leurs Applications - ENS Cachan - École normale supérieure - Cachan - CNRS - Centre National de la Recherche Scientifique)

  • Hassan Maatouk

    (ENSM ST-ETIENNE - Ecole Nationale Supérieure des Mines de St Etienne)

  • Didier Rullière

    (LSAF - Laboratoire de Sciences Actuarielle et Financière - UCBL - Université Claude Bernard Lyon 1 - Université de Lyon)

Abstract

This paper collects the contributions which were presented during the session devoted to Gaussian processes at the Journées MAS 2016. First, an introduction to Gaussian processes is provided, and some current research questions are discussed. Then, an application of Gaussian process modeling under linear inequality constraints to financial data is presented. Also, an original procedure for handling large data sets is described. Finally, the case of Gaussian process based iterative optimization is discussed.

Suggested Citation

  • François Bachoc & Emile Contal & Hassan Maatouk & Didier Rullière, 2017. "Gaussian processes for computer experiments," Post-Print hal-01665936, HAL.
    • Handle: RePEc:hal:journl:hal-01665936
    DOI: 10.1051/proc/201760163
    Note: View the original document on HAL open archive server: https://hal.science/hal-01665936v1
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    References listed on IDEAS

    as
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    10. Bachoc, François, 2014. "Asymptotic analysis of the role of spatial sampling for covariance parameter estimation of Gaussian processes," Journal of Multivariate Analysis, Elsevier, vol. 125(C), pages 1-35.
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    Cited by:

    1. Reding, Lucas & López-Lopera, Andrés F. & Bachoc, François, 2025. "Covariance parameter estimation of Gaussian processes with approximated functional inputs," Journal of Multivariate Analysis, Elsevier, vol. 205(C).

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