predict_separable_2dim_MS | R Documentation |

This function computes fast computation when the test points lie on a 2D lattice for multiple sources of observations.

```
predict_separable_2dim_MS(object, testing_input_separable,
X_testing=NULL, math_model=NULL,...)
```

`object` |
an object of class |

`testing_input_separable` |
a list of two. In the first (outer) list, each list is a source of test input. Then in the second (interior) list, The first element is a vector of the coordinate of the latitue and the second element is a vector of the coordinate of the longitude. |

`X_testing` |
mean/trend for prediction where the defaul value is NULL. |

`math_model` |
a list of functions of math models to be calibrated. |

The returned value is a S4 CLass `predictobj.rcalibration_MS`

.

Mengyang Gu [aut, cre]

Maintainer: Mengyang Gu <mengyang@pstat.ucsb.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.

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

M. Gu and L. Wang (2018) *Scaled Gaussian Stochastic Process for Computer Model Calibration and Prediction*. SIAM/ASA Journal on Uncertainty Quantification, **6**, 1555-1583.

M. Gu (2019) *Jointly Robust Prior for Gaussian Stochastic Process in Emulation, Calibration and Variable Selection
*. Bayesian Analysis, **14**, 857-885.

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