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.
alpha.star(x, data, debug = FALSE)
an object of class bn
(for bn.fit
and custom.fit
)
or an object of class bn.fit
(for bn.net
).
a data frame containing the variables in the model.
a boolean value. If TRUE
a lot of debugging output is
printed; otherwise the function is completely silent.
alpha.star()
returns a positive number, the estimated optimal imaginary
sample size value.
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.
# 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
# }
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