SubGroupSeparation is an R package for marginalization in Bayesian networks. It allows for efficient exact and approximate inference that works both in low- and high-dimensional settings. As illustrated below, efficient marginalization is reached by splitting the calculation into sub-calculations of lower dimensionality.
In order to install the package, it suffices to launch
R CMD INSTALL path/to/SubGroupSeparation
from a terminal, or make install from within the package source folder.
SubGroupSeparation requires R >= 3.5, and depends on
bitops and
methods. Other packages are requested in
order to plot graphs, but are not mandatory.
library(SubGroupSeparation)
# create BN and label variables
set.seed(6)
myBayesNet <- randomBN(3)
myBayesNet@variables <- c("rain", "sprinkler", "wet grass")
plot(myBayesNet)
# what's the probability of having rain and wet grass at the same time?
# define observed variables and calculate marginal probability
myObserved <- list(observed.vars=c("rain", "wet grass"), observed.vals=c(2,2))
exactInference(myBayesNet,myObserved)
