Description Objects from the Class Slots Author(s) References See Also

S4 class for prediction after Robust rcalibration for multiple sources.

Objects of this class are created and initialized with the function `predict_MS`

that computes the prediction and the uncertainty quantification.

`mean`

:object of class

`list`

. Each element is a`vector`

of the predictive mean at testing inputs combing the mathematical model and discrepancy function for each source.`math_model_mean`

:object of class

`list`

. Each element is a`vector`

of the predictive mean at testing inputs using only the mathematical model (and the trend if specified).`math_model_mean_no_trend`

:object of class

`list`

. Each element is a`vector`

of the predictive mean at testing inputs using only the mathematical model without the trend for each source.`interval`

:object of class

`list`

. Each element is a`matrix`

of the upper and lower predictive credible interval. If interval_data is TRUE in the`predict_MS`

, the experimental noise is included for computing the predictive credible interval.

Mengyang Gu [aut, cre]

Maintainer: Mengyang Gu <mgu6@jhu.edu>

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.

`predict_MS`

for more details about how to do prediction for a `rcalibration_MS`

object.

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