learn.params: learn the parameters of a BN.

In tavazzie/bnstructScore: Bayesian Network Structure Learning from Data with Missing Values

Description

Learn the parameters of a BN object according to a BNDataset using MAP (Maximum A Posteriori) estimation.

Usage

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)

Arguments

bn	a BN object.
dataset	a BNDataset object.
ess	Equivalent Sample Size value.
use.imputed.data	use imputed data.

Details

Parameter learning is not possible in case of networks learnt using the mmpc algorithm, or from bootstrap samples, as there may be loops.

Value

new BN object with conditional probabilities.

See Also

learn.network

Examples

## 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)

tavazzie/bnstructScore documentation built on Dec. 23, 2021, 7:47 a.m.