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rbn {bnlearn}  R Documentation 
Simulate random samples from a given Bayesian network
Description
Simulate random samples from a given Bayesian network.
Usage
## S3 method for class 'bn' rbn(x, n = 1, data, fit = "mle", ..., debug = FALSE) ## S3 method for class 'bn.fit' rbn(x, n = 1, ..., debug = FALSE)
Arguments
x 
an object of class 
n 
a positive integer giving the number of observations to generate. 
data 
a data frame containing the data the Bayesian network was learned from. 
fit 
a character string, the label of the method used to fit the parameters of the newtork. See

... 
additional arguments for the parameter estimation prcoedure, see again 
debug 
a boolean value. If 
Details
rbn()
implements forward/logic sampling: values for the root nodes are sampled from their
(unconditional) distribution, then those of their children conditional on the respective parent sets. This is
done iteratively until values have been sampled for all nodes.
If x
contains NA
parameter estimates (because of unobserved discrete parents
configurations in the data the parameters were learned from), rbn
will produce samples that
contain NA
s when those parents configurations appear in the simulated samples. See bn.fit
for details on how to make sure bn.fit
objects contain no
NA
parameter estimates.
Value
A data frame with the same structure (column names and data types) of the data
argument (if
x
is an object of class bn
) or with the same structure as the data originally used to
to fit the parameters of the Bayesian network (if x
is an object of class
bn.fit
).
Author(s)
Marco Scutari
References
Korb K, Nicholson AE (2010). Bayesian Artificial Intelligence. Chapman & Hall/CRC, 2nd edition.
See Also
Examples
## Not run: data(learning.test) res = gs(learning.test) res = set.arc(res, "A", "B") par(mfrow = c(1,2)) plot(res) sim = rbn(res, 500, learning.test) plot(gs(sim)) ## End(Not run)
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