BNSL (version 0.1.4)

bnsl: Bayesian Network Structure Learning

Description

The function outputs the Bayesian network structure given a dataset based on an assumed criterion.

Usage

bnsl(df, tw = 0, proc = 1, s=0, n=0, ss=1)

Arguments

df

a dataframe.

tw

the upper limit of the parent set.

proc

the criterion based on which the BNSL solution is sought. proc=1,2, and 3 indicates that the structure learning is based on Jeffreys [1], MDL [2,3], and BDeu [3]

s

The value computed when obtaining the bound.

n

The number of samples.

ss

The BDeu parameter.

Value

The Bayesian network structure in the bn class of bnlearn.

References

[1] Suzuki, J. ``An Efficient Bayesian Network Structure Learning Strategy", New Generation Computing, December 2016. [2] Suzuki, J. ``A construction of Bayesian networks from databases based on an MDL principle", Uncertainty in Artificial Intelligence, pages 266-273, Washington D.C. July, 1993. [3] Suzuki, J. ``Learning Bayesian Belief Networks Based on the Minimum Description Length Principle: An Efficient Algorithm Using the B & B Technique", International Conference on Machine Learning, Bali, Italy, July 1996" [4] Suzuki, J. ``A Theoretical Analysis of the BDeu Scores in Bayesian Network Structure Learning", Behaviormetrika 1(1):1-20, [5] Suzuki, J. and Kawahara, J., ``Branch and Bound for Regular Bayesian Network Structure learning", Uncertainty in Artificial Intelligence, pages 212-221, Sydney, Australia, August 2017. [6] Suzuki, J. ``Forest Learning from Data and its Universal Coding", IEEE Transactions on Information Theory, Dec. 2018. January 2017.

See Also

parent

Examples

# NOT RUN {
library(bnlearn)
bnsl(asia)
# }