bnsl: Bayesian Network Structure Learning

In 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.

Author(s)

Joe Suzuki and Jun Kawahara

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

library(bnlearn)
bnsl(asia)

Example output

Loading required package: bnlearn

Attaching package: 'bnlearn'

The following object is masked from 'package:stats':

    sigma

Loading required package: igraph

Attaching package: 'igraph'

The following objects are masked from 'package:bnlearn':

    compare, degree, path, subgraph

The following objects are masked from 'package:stats':

    decompose, spectrum

The following object is masked from 'package:base':

    union

goal: 11111111, 11097.1, 11097.1, 7

  Random/Generated Bayesian network

  model:
   [A][T][L][S|L][E|T:L][B|S:T][X|E][D|B:E] 
  nodes:                                 8 
  arcs:                                  8 
    undirected arcs:                     0 
    directed arcs:                       8 
  average markov blanket size:           2.75 
  average neighbourhood size:            2.00 
  average branching factor:              1.00 

  generation algorithm:                  Empty 

BNSL documentation built on May 2, 2019, 7:58 a.m.