Learning Bayesian Networks with the bnlearn R Package

被引:1185
作者
Scutari, Marco [1 ]
机构
[1] Univ Padua, Dept Stat Sci, I-35121 Padua, Italy
关键词
bayesian networks; R; structure learning algorithms; constraint-based algorithms; score-based algorithms; conditional independence tests;
D O I
10.18637/jss.v035.i03
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
bnlearn is an R package (R Development Core Team 2010) which includes several algorithms for learning the structure of Bayesian networks with either discrete or continuous variables. Both constraint-based and score-based algorithms are implemented, and can use the functionality provided by the snow package (Tierney et al. 2008) to improve their performance via parallel computing. Several network scores and conditional independence algorithms are available for both the learning algorithms and independent use. Advanced plotting options are provided by the Rgraphviz package (Gentry et al. 2010).
引用
收藏
页码:1 / 22
页数:22
相关论文
共 40 条
[1]   A comparison of learning algorithms for Bayesian networks:: a case study based on data from an emergency medical service [J].
Acid, S ;
de Campos, LM ;
Fernández-Luna, JM ;
Rodríguez, S ;
Rodríguez, JM ;
Salcedo, JL .
ARTIFICIAL INTELLIGENCE IN MEDICINE, 2004, 30 (03) :215-232
[2]  
[Anonymous], 1994, P 10 C UNC ART INT
[3]  
[Anonymous], **NON-TRADITIONAL**
[4]  
[Anonymous], 2010, R LANG ENV STAT COMP
[5]  
[Anonymous], 2005, J. Statist. Softw.
[6]  
[Anonymous], 1980, Multivariate Analysis
[7]  
[Anonymous], BAYESIAN ARTIFICIAL
[8]  
[Anonymous], 2007, P 2007 UK WORKSH COM
[9]  
[Anonymous], 2007, J STAT SOFTW
[10]  
Boettcher SG., 2003, J STAT SOFTW, V8, P1, DOI [DOI 10.18637/JSS.V008.I20, 10.18637/jss.v008.i20]