Based on the result list from `glmBayesMfp`

, sample from the
Bayesian model average (BMA) over the models contained in this list.

1 2 | ```
sampleBma(object, mcmc = McmcOptions(), postProbs = posteriors(object),
nMargLikSamples = NULL, verbose = TRUE, ...)
``` |

`object` |
valid |

`mcmc` |
MCMC options object with class |

`postProbs` |
vector of posterior probabilites (will be normalized within
the function) for the weighting of the models in |

`nMargLikSamples` |
If this is non- |

`verbose` |
should information on computation progress be given? (default) |

`...` |
optional further arguments already available for sampling from
a single model: |

If TBF methodology is used (which is specified within the `glmBayesMfp`

object), then Monte Carlo (MC) sampling is used. If the fully Bayesian,
generalized hyper-g prior methodology is used, then the sampling is based on
MCMC. Therefore, instead of only specifying the required number of samples
and the model probabilities, one also needs to specify the burn-in length
and the thinning parameter, which will be applied to every model from which
at least one sample is included in the average. Alternatively, you can ask
for MCMC marginal likelihood estimates for all models in the list. Then at
least `nMargLikSamples`

will be produced for each model, whether
included in the BMA sample or not.

The result is a list with the following elements:

- modelData
data frame containing the result from the

`as.data.frame`

function, and in addition BMA probabilities, BMA frequencies in the sample, acceptance ratios of the MCMC runs and optionally marginal likelihood estimates / standard errors.- samples
an object of S4 class

`GlmBayesMfpSamples`

containing the samples from the BMA.

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