learn.params | R Documentation |
Learn the parameters of a BN object according to a BNDataset using MAP (Maximum A Posteriori) estimation.
learn.params(bn, dataset, ess = 1, use.imputed.data = F) ## S4 method for signature 'BN,BNDataset' learn.params(bn, dataset, ess = 1, use.imputed.data = FALSE)
bn |
a |
dataset |
a |
ess |
Equivalent Sample Size value. |
use.imputed.data |
use imputed data. |
Parameter learning is not possible in case of networks learnt using the mmpc
algorithm,
or from bootstrap samples, as there may be loops.
new BN
object with conditional probabilities.
learn.network
## Not run: ## first create a BN and learn its structure from a dataset dataset <- BNDataset("file.header", "file.data") bn <- BN(dataset) bn <- learn.structure(bn, dataset) bn <- learn.params(bn, dataset, ess=1) ## End(Not run)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.