bnsl {BNSL} | R Documentation |
The function outputs the Bayesian network structure given a dataset based on an assumed criterion.
bnsl(df, tw = 0, proc = 1, s=0, n=0, ss=1)
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. |
The Bayesian network structure in the bn class of bnlearn.
Joe Suzuki and Jun Kawahara
[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.
parent
library(bnlearn)
bnsl(asia)