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The effect of the nugget on Gaussian process emulators of computer models

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  • Andrianakis, Ioannis
  • Challenor, Peter G.

Abstract

The effect of a Gaussian process parameter known as the nugget, on the development of computer model emulators is investigated. The presence of the nugget results in an emulator that does not interpolate the data and attaches a non-zero uncertainty bound around them. The limits of this approximation are investigated theoretically, and it is shown that they can be as large as those of a least squares model with the same regression functions as the emulator, regardless of the nugget’s value. The likelihood of the correlation function parameters is also studied and two mode types are identified. Type I modes are characterised by an approximation error that is a function of the nugget and can therefore become arbitrarily small, effectively yielding an interpolating emulator. Type II modes result in emulators with a constant approximation error. Apart from a theoretical investigation of the limits of the approximation error, a practical method for automatically imposing restrictions on its extent is introduced. This is achieved by means of a penalty term that is added to the likelihood function, and controls the amount of unexplainable variability in the computer model. The main findings are illustrated on data from an Energy Balance climate model.

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  • Andrianakis, Ioannis & Challenor, Peter G., 2012. "The effect of the nugget on Gaussian process emulators of computer models," Computational Statistics & Data Analysis, Elsevier, vol. 56(12), pages 4215-4228.
    • Handle: RePEc:eee:csdana:v:56:y:2012:i:12:p:4215-4228
    DOI: 10.1016/j.csda.2012.04.020
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    References listed on IDEAS

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    1. Berger J.O. & De Oliveira V. & Sanso B., 2001. "Objective Bayesian Analysis of Spatially Correlated Data," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1361-1374, December.
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    3. Carmack, Patrick S. & Spence, Jeffrey S. & Schucany, William R. & Gunst, Richard F. & Lin, Qihua & Haley, Robert W., 2012. "A new class of semiparametric semivariogram and nugget estimators," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 1737-1747.
    4. Marc C. Kennedy & Anthony O'Hagan, 2001. "Bayesian calibration of computer models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(3), pages 425-464.
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    Cited by:

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    2. Slot, René M.M. & Sørensen, John D. & Sudret, Bruno & Svenningsen, Lasse & Thøgersen, Morten L., 2020. "Surrogate model uncertainty in wind turbine reliability assessment," Renewable Energy, Elsevier, vol. 151(C), pages 1150-1162.
    3. Ioannis Andrianakis & Ian R Vernon & Nicky McCreesh & Trevelyan J McKinley & Jeremy E Oakley & Rebecca N Nsubuga & Michael Goldstein & Richard G White, 2015. "Bayesian History Matching of Complex Infectious Disease Models Using Emulation: A Tutorial and a Case Study on HIV in Uganda," PLOS Computational Biology, Public Library of Science, vol. 11(1), pages 1-18, January.
    4. Garbuno-Inigo, A. & DiazDelaO, F.A. & Zuev, K.M., 2016. "Gaussian process hyper-parameter estimation using Parallel Asymptotically Independent Markov Sampling," Computational Statistics & Data Analysis, Elsevier, vol. 103(C), pages 367-383.
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    6. 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.

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