Description Usage Arguments Value Note Author(s)

Generate a Markov Chain of the parameters in the correlation function Using Metropolis-Hastings within Gibbs

1 2 | ```
MCMCMetropolisGibbs(inputs, outputs, fn, H, MCMC.iterations, starting.values,
proposal.sd = 0.1, cor.function, MC.plot = TRUE, ...)
``` |

`inputs` |
A data frame, matrix or vector containing the input values of the training data. |

`outputs` |
A data frame, matrix or vector containing the output values of the training data. |

`fn` |
A function used to maximise the negetive log likelihood |

`H` |
A matrix of prior mean regressors from the training data |

`MCMC.iterations` |
The number of iterations that MCMC should be run for |

`starting.values` |
the starting values for which the MCMC can start running |

`proposal.sd` |
is the standard deviation of the random walk proposal (default |

`cor.function` |
Specifies a correlation function used as part of the prior information for the emulator.
This package has options of: |

`MC.plot` |
If |

`...` |
additional arguments to be passed on to correlation functions (see |

The function returns a list containting the following components:

`density.sample` | The negetive log likelihood of the MCMC output at the starting value and at each iteration |

`theta.sample` | A matrix of the theta sample at the starting value and at each iteration |

Note that this function first calculates the negetive log likelihood of the starting values so returns `MCMC.iterations`

+ 1 values.

Originally written by Jeremy Oakley. Modified by Sajni Malde

OakleyJ/MUCM documentation built on May 7, 2019, 9:01 p.m.

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