Description Usage Arguments Value Author(s) References See Also

This function samples the initial hyperparameters and parameters that are needed for the MCMC simulation.

1 | ```
sampleParms(X, GLOBvar, HYPERvar, s_init = NULL, options)
``` |

`X` |
Input data. |

`GLOBvar` |
Global variables of the MCMC simulation. |

`HYPERvar` |
Hyperparameter variables. |

`s_init` |
Initial number of changepoints. |

`options` |
MCMC options, as given by e.g. |

Returns a list with elements:

`E` |
The initial changepoint vector. |

`S` |
The intial networks structure. |

`B` |
The initial regression parameters. |

`Sig2` |
The inital sigma squared variances. |

`betas` |
The intial hyperparameters for the exponential information sharing prior. |

`hyper_params` |
The initial hyperparameters for the binomial information sharing prior. |

Sophie Lebre

Frank Dondelinger

For more information about the parameters and hyperparameters, see:

Dondelinger et al. (2012), "Non-homogeneous dynamic Bayesian networks with Bayesian regularization for inferring gene regulatory networks with gradually time-varying structure", Machine Learning.

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