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cpquery {bnlearn} R Documentation

Perform conditional probability queries

Description

Perform conditional probability queries (CPQs).

Usage

cpquery(fitted, event, evidence, method = "ls", ..., debug = FALSE)

Arguments

fitted an object of class bn.fit.
event, evidence see below.
method a character string, the method used to perform the conditional probability query. Currently only Logic Sampling is implemented.
... additional tuning parameters.
debug a boolean value. If TRUE a lot of debugging output is printed; otherwise the function is completely silent.

Value

A numeric value, the conditional probability of event conditional on evidence.

Logic Sampling

The event and evidence arguments must be two expressions describing the event of interest and the conditioning evidence in a format such that, if we denote with data the data set the network was learned from, data[evidence, ] and data[event, ] return the correct observations. If either parameter is equal to TRUE an unconditional probability query is performed.

Two tuning parameters are available:

  • n: a positive integer number, the number of random observations to generate from fitted. Defaults to 5000 * nparams(fitted).
  • batch: a positive integer number, the size of each batch of random observations. Defaults to 10^4.

Author(s)

Marco Scutari

References

Korb K, Nicholson AE (2003). Bayesian Artificial Intelligence. Chapman & Hall/CRC.

Examples

fitted = bn.fit(hc(learning.test), learning.test)
cpquery(fitted, (B == "b"), (A == "a"))
# the result should be around 0.025.
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