Description Usage Arguments Value Author(s) References See Also
This function initialises the variabes for the MCMC simulation, runs the simulation and returns the output.
1 2 3 4 |
targetdata |
Target input data: A matrix of dimensions NumNodes by NumTimePoints. |
preddata |
Optional: Input response data, if different from the target data. |
q |
Number of nodes. |
n |
Number of timepoints. |
multipleVar |
|
minPhase |
Minimal segment length. |
niter |
Number of MCMC iterations. |
scaling |
If |
method |
Network structure prior to use: |
prior.params |
Initial hyperparameters for the information sharing prior. |
self.loops |
If |
k |
Initial value for the level-2 hyperparameter of the exponential information sharing prior. |
options |
MCMC options as obtained e.g. by the function
|
outputFile |
File where the output of the MCMC simulation should be saved. |
fixed.edges |
Matrix of size NumNodes by NumNodes, with
|
A list containing the results of the MCMC simulation: network
samples, changepoint samples and hyperparameter samples. For details, see
output
.
Sophie Lebre
Frank Dondelinger
For more information about the MCMC simulations, 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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