R: Simulate random data from a given Bayesian network

rbn {bnlearn} | R Documentation

## Simulate random data from a given Bayesian network

### Description

Simulate random data 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 `bn` or `bn.fit`.
`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 `bn.fit` for details.
`...` | additional arguments for the parameter estimation prcoedure, see again `bn.fit` for details..
`debug` | a boolean value. If `TRUE` a lot of debugging output is printed; otherwise the function is completely silent.

### 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

`bn.boot`, `bn.cv`.

### 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)

[Package bnlearn version 4.3 Index]