predictobj.rcalibration-class {RobustCalibration}

Predictive results for the Robust Calibration class

Description

S4 class for prediction after Robust rcalibration with or without the specification of the discrepancy model.

Objects from the Class

Objects of this class are created and initialized with the function predict that computes the prediction and the uncertainty quantification.

Slots

mean:

object of class vector. The predictive mean at testing inputs combing the mathematical model and discrepancy function.

math_model_mean:

object of class vector. The predictive mean at testing inputs using only the mathematical model (and the trend if specified).

math_model_mean_no_trend:

object of class vector. The predictive mean at testing inputs using only the mathematical model without the trend.

delta:

object of class vector. The predictive discrepancy function.

interval:

object of class matrix. The upper and lower predictive credible interval. If interval_data is TRUE in the predict.rcalibration, the experimental noise is included for computing the predictive credible interval.

Author(s)

Mengyang Gu [aut, cre]

Maintainer: Mengyang Gu <[e-mail]>

References

A. O'Hagan and M. C. Kennedy (2001), Bayesian calibration of computer models, Journal of the Royal Statistical Society: Series B (Statistical Methodology, 63, 425-464.

M. Gu (2016), Robust Uncertainty Quantification and Scalable Computation for Computer Models with Massive Output, Ph.D. thesis., Duke University.

M. Gu and L. Wang (2017) Scaled Gaussian Stochastic Process for Computer Model Calibration and Prediction. arXiv preprint arXiv:1707.08215.

See Also

predict.rcalibration for more details about how to do prediction for a rcalibration object.