bnlearn (version 4.4.1)

alpha.star: Estimate the optimal imaginary sample size for BDe(u)

Description

Estimate the optimal value of the imaginary sample size for the BDe score, assuming a uniform prior and given a network structure and a data set.

Usage

alpha.star(x, data, debug = FALSE)

Arguments

x

an object of class bn (for bn.fit and custom.fit) or an object of class bn.fit (for bn.net).

data

a data frame containing the variables in the model.

debug

a boolean value. If TRUE a lot of debugging output is printed; otherwise the function is completely silent.

Value

alpha.star() returns a positive number, the estimated optimal imaginary sample size value.

References

Steck H (2008). "Learning the Bayesian Network Structure: Dirichlet Prior versus Data". Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, pp. 511--518.

Examples

# NOT RUN {
data(learning.test)
dag = hc(learning.test, score = "bic")

for (i in 1:3) {

  a = alpha.star(dag, learning.test)
  dag = hc(learning.test, score = "bde", iss = a)

}#FOR
# }