Description Usage Arguments Value Author(s) References Examples
View source: R/lweights_gaussian.R
The function computes the log-weights of all edges in a gaussian setting.
The result should be used in edge.prob with argument log
set to TRUE
.
Usual values are used as default for the prior normal-Wishart hyperparameters.
Computation can be parallelized by setting nbcores
to more than 2. Parallelization relies on parallel
.
1 2 3 4 5 6 7 |
data |
Matrix containing continuous data. |
a |
Prior degree of freedom of the normal-Wishart distribution. |
mu |
Prior mean for the mean of the normal-Wishart distribution. |
au |
Prior relative precision of the normal-Wishart distribution. |
T |
Prior scale matrix of the normal-Wishart distribution. |
nbcores |
Number of cores to be used in parallelized computation. |
W |
log-weight matrix |
Lo<c3><af>c Schwaller
This package implements the method described in the paper "Bayesian Inference of Graphical Model Structures Using Trees" by L. Schwaller, S. Robin, M. Stumpf, 2015 (submitted and availavable on arXiv).
1 2 3 4 5 6 | library('saturnin')
data(data_gaussian)
W <- lweights_gaussian(data_gaussian)
prob <- edge.prob(W, log = TRUE)
|
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