Description Usage Arguments Details Value Author(s) See Also

View source: R/logDenGaussion.R

Invokes the model and calculate an unnormalized logDensity (-1/2*misfit) assuming (multivariate) Gaussian errors in both data in priors.

1 2 3 4 5 |

`theta` |
the parameter vector to be optimized (may be more than updated, if used with blocks). |

`logDenAccept` |
scalar: logDen for parms from revious run for two step Metropolis decision |

`metropolisStepTemp` |
numeric named vector: the temperature for internal metropolis step |

`...` |
any other arguments passed to fModel |

`fModel` |
the model function, which predicts the output based on theta |

`theta0` |
parameter vector, first argument to fModel. Before invocation components theta overwrite theta0 |

`obs` |
vector of data to compare with |

`invCovar` |
the inverse of the Covariance of obs (its uncertainty) << alternatively a vector of variances (diagonal covariance matrix) can be supplied and calculation is much more efficient |

`thetaPrior` |
the prior estimate of the parameters |

`invCovarTheta` |
the inverse of the Covariance of the prior parameter estimates << alternatively a vector of variances (diagonal covariance matrix) can be supplied and calculation is much more efficient |

`namesTheta` |
names assigned to theta (if not NULL), before invoking mofModel |

`blockIndices` |
integer vector: index of the components in theta and theta0 that should be regarded in this block |

`scale` |
factor to mulitply the misfit (e.g. -1/2 to obtain the unnormalized logDensity) |

If thetaPrior is not specified (NULL) then no penalty is assigned to parameters.

Supports a two-step Metropolis descision. If `logDenAccept["parms"]`

is provided,
then a Metropolis descision is done based only on the parameters.
If it fails, then `c(obs=NA, parms=-Inf)`

is returned.
The possible costly evaluation of fModel is avoided.

the misfit: scale *( t(tmp.diffObs) %*% invCovar %*% tmp.diffObs + t(tmp.diffParms) %*% invCovarTheta %*% tmp.diffParms )

Thomas Wutzler

`twDEMCBlockInt`

`dummyTwDEMCModel`

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