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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

    (SAF - 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-01665936
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    References listed on IDEAS

    as
    1. Cousin, Areski & Maatouk, Hassan & Rullière, Didier, 2016. "Kriging of financial term-structures," European Journal of Operational Research, Elsevier, vol. 255(2), pages 631-648.
    2. Areski Cousin & Ibrahima Niang, 2014. "On the Range of Admissible Term-Structures," Working Papers hal-00968943, HAL.
    3. Areski Cousin & Ibrahima Niang, 2014. "On the range of admissible term-structures," Papers 1404.0340, arXiv.org.
    4. Kaufman, Cari G. & Schervish, Mark J. & Nychka, Douglas W., 2008. "Covariance Tapering for Likelihood-Based Estimation in Large Spatial Data Sets," Journal of the American Statistical Association, American Statistical Association, vol. 103(484), pages 1545-1555.
    5. Lars E.O. Svensson, 1994. "Estimating and Interpreting Forward Interest Rates: Sweden 1992 - 1994," NBER Working Papers 4871, National Bureau of Economic Research, Inc.
    6. Cressie, N. & Lahiri, S. N., 1993. "The Asymptotic Distribution of REML Estimators," Journal of Multivariate Analysis, Elsevier, vol. 45(2), pages 217-233, May.
    7. 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.
    8. Bachoc, François, 2013. "Cross Validation and Maximum Likelihood estimations of hyper-parameters of Gaussian processes with model misspecification," Computational Statistics & Data Analysis, Elsevier, vol. 66(C), pages 55-69.
    9. Robert L. Winkler, 1981. "Combining Probability Distributions from Dependent Information Sources," Management Science, INFORMS, vol. 27(4), pages 479-488, April.
    10. Noel Cressie & Gardar Johannesson, 2008. "Fixed rank kriging for very large spatial data sets," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(1), pages 209-226, February.
    11. Yakowitz, S. J. & Szidarovszky, F., 1985. "A comparison of kriging with nonparametric regression methods," Journal of Multivariate Analysis, Elsevier, vol. 16(1), pages 21-53, February.
    12. Paulo, Rui & García-Donato, Gonzalo & Palomo, Jesús, 2012. "Calibration of computer models with multivariate output," Computational Statistics & Data Analysis, Elsevier, vol. 56(12), pages 3959-3974.
    13. Nelson, Charles R & Siegel, Andrew F, 1987. "Parsimonious Modeling of Yield Curves," The Journal of Business, University of Chicago Press, vol. 60(4), pages 473-489, October.
    14. Ying, Zhiliang, 1991. "Asymptotic properties of a maximum likelihood estimator with data from a Gaussian process," Journal of Multivariate Analysis, Elsevier, vol. 36(2), pages 280-296, February.
    15. Robert L. Winkler, 1968. "The Consensus of Subjective Probability Distributions," Management Science, INFORMS, vol. 15(2), pages 61-75, October.
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