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

In bnlearn: Bayesian Network Structure Learning, Parameter Learning and Inference

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.

Author(s)

Marco Scutari

References

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

Examples

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

bnlearn documentation built on Sept. 11, 2024, 8:27 p.m.