Sample_sigma_2_theta_m_no_discrepancy | R Documentation |

This function samples the variance and mean parameters assuming no discrepancy function.

Sample_sigma_2_theta_m_no_discrepancy(param, output, p_theta, X, have_mean, inv_output_weights, cm_obs, S_2_f,num_obs_all)

`param` |
Current parameters in the MCMC. |

`output` |
Experimental observations. |

`p_theta` |
Number of calibration parameters. |

`X` |
Number of mean discrepancy parameters. |

`have_mean` |
Whether the mean discrepancy is zero or not. |

`inv_output_weights` |
The inverse of the weights of outputs.. |

`cm_obs` |
outputs from the mathematical model. |

`S_2_f` |
Variance of the data. This term is useful when there are repeated experiments. |

`num_obs_all` |
Total number of observations. If there is no repeated experiment, this is equal to the number of observable inputs. |

A vector of samples of the variance and trend parameters.

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 (2017) *Scaled Gaussian Stochastic Process for Computer Model Calibration and Prediction*. arXiv preprint arXiv:1707.08215.

M. Gu (2018) *Jointly Robust Prior for Gaussian Stochastic Process in Emulation, Calibration and Variable Selection
*. arXiv preprint arXiv:1804.09329.

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