Evaluate the functional (mean) response for the 2-d
exponential data (truth) at the
X inputs, and randomly
Z–values having normal error with standard
Must be a
Standard deviation of iid normal noise added to the responses
The response is evaluated as
Z(X) = X1 * exp(-X1^2-X2^2),
thus creating the outputs
Zero-mean normal noise with
sd=0.001 is added to the
Output is a
data.frame with columns:
Numeric vector describing the responses (with noise) at the
Numeric vector describing the true responses (without
noise) at the
Gramacy, R. B. (2020) Surrogates: Gaussian Process Modeling, Design and Optimization for the Applied Sciences. Boca Raton, Florida: Chapman Hall/CRC. https://bobby.gramacy.com/surrogates/
Gramacy, R. B. (2007). tgp: An R Package for Bayesian Nonstationary, Semiparametric Nonlinear Regression and Design by Treed Gaussian Process Models. Journal of Statistical Software, 19(9). https://www.jstatsoft.org/v19/i09
Robert B. Gramacy, Matthew Taddy (2010). Categorical Inputs, Sensitivity Analysis, Optimization and Importance Tempering with tgp Version 2, an R Package for Treed Gaussian Process Models. Journal of Statistical Software, 33(6), 1–48. https://www.jstatsoft.org/v33/i06/.
Gramacy, R. B., Lee, H. K. H. (2008). Bayesian treed Gaussian process models with an application to computer modeling. Journal of the American Statistical Association, 103(483), pp. 1119-1130. Also available as ArXiv article 0710.4536 https://arxiv.org/abs/0710.4536
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