learn.params: learn the parameters of a BN.

Description Usage Arguments Details Value See Also Examples

Description

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

Usage

1
2
3
4
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

1
2
3
4
5
6
7
8
## 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)

bnstruct documentation built on July 2, 2020, 2:26 a.m.