bnlearn: alpha.star – R documentation

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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.

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

Bayesian Network Structure Learning, Parameter Learning and Inference

v4.6.1

GPL (>= 2)

Authors

Marco Scutari [aut, cre], Robert Ness [ctb]

Initial release

2020-09-16