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 BN,BNDataset
learn.params(bn, dataset, ess = 1, use.imputed.data = FALSE)
new BN object with conditional probabilities.
a BN object.
a BNDataset object.
Equivalent Sample Size value.
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.
learn.network
if (FALSE) {
## 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)
}
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