ComputeKLDs: Compute signed and symmetric Kullback-Leibler divergence
In Yam76/BayesNetBP: Bayesian Network Belief Propagation

Description Usage Arguments Details Value Author(s) Examples

Compute signed and symmetric Kullback-Leibler divergence of variables over a spectrum of evidence

1	ComputeKLDs(tree, var0, vars, seq, pbar = TRUE, method = "gaussian")

`tree`	a `ClusterTree` object
`var0`	the variable to have evidence absrobed
`vars`	the variables to have divergence computed
`seq`	a `vector` of numeric values as the evidences
`pbar`	`logical(1)` whether to show progress bar
`method`	method for divergence computation: `gaussian` for Gaussian approximation, for Monte Carlo integration

Compute signed and symmetric Kullback-Leibler divergence of variables over a spectrum of evidence. The signed and symmetric Kullback-Leibler divergence is also known as Jeffery's signed information (JSI) for continuous variables.

a data.frame of the divergence

Han Yu

## Not run: 
data(liver)
tree.init.p <- Initializer(dag=liver$dag, data=liver$data, 
                           node.class=liver$node.class, 
                           propagate = TRUE)
klds <- ComputeKLDs(tree=tree.init.p, var0="Nr1i3", 
                    vars=setdiff(tree.init.p@node, "Nr1i3"),
                    seq=seq(-3,3,0.5))
head(klds)

## End(Not run)