Description Usage Arguments Value

Run a Metropolis Hastings within Gibbs algorithm and a Metropolis Hastings algorithm with the covariance matrix estimated on the the sample set generated in the Metropolis within Gibbs. This algorithm is suitable only for models with discrepancy.

1 2 | ```
MetropolisHastingsCppD(Ngibbs, Nmh, theta_init, r, SIGMA, Yf, binf, bsup,
LogTest, stream, tt)
``` |

`Ngibbs` |
the number of iteration in the Metropolis within Gibbs |

`Nmh` |
the number of iteration in the Metropolis Hastings |

`theta_init` |
the starting point |

`r` |
regulation percentage in the modification of the k |

`SIGMA` |
the covariance of the proposition distribution |

`Yf` |
the vector of recorded data |

`binf` |
the lower bound of the parameters to calibrate |

`bsup` |
the upper bound of the parameters to calibrate |

`LogTest` |
the log posterior density distribution |

`stream` |
(default=1) if stream=0 the progress bar is disabled |

list of outputs:

PHIwg the points of the Metropolis within Gibbs algorithm in the transformed space

PHI the points of the Metropolis Hastings algorithm in the transformed space

THETAwg the points of the Metropolis within Gibbs algorithm in the real space

THETA the points of the Metropolis Hastings algorithm in the real space

AcceptationRatioWg the vector of the acceptance ratio for each parameter in the Metropolis within Gibbs

AcceptationRatio the acceptance ratio in the Metropolis Hastings

S the covariance computed after the Metropolis within Gibbs

LikeliWG the likelihood computed at each iteration of the Metropolis within Gibbs algorithm

Likeli the likelihood computed at each iteration of the Metropolis Hastings algorithm

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