Simulate random samples from a given Bayesian network
Simulate random samples from a given Bayesian network.
## 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)
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 |
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 NAs 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.
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).
Marco Scutari
Korb K, Nicholson AE (2010). Bayesian Artificial Intelligence. Chapman & Hall/CRC, 2nd edition.
## 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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