BuildTable: Builds the conditional probability table for a Pnode

In ralmond/Peanut: Parameterized Bayesian Networks, Abstract Classes

Builds the conditional probability table for a Pnode

Description

The function BuildTable builds the conditional probability table for a Pnode object, and sets the prior weight for the node using the current values of parameters. It sets these in the Bayesian network object as appropriate for the implementation. The expression BuildAllTables(net) builds tables for all of the nodes in PnetPnodes(net).

Usage

BuildTable(node)
BuildAllTables(net, debug=FALSE)

Arguments

node	A Pnode object whose table is to be built.
net	A Pnet object for whom the tables are needed to be built.
debug	A logical scalar. If true then recover is called after an error, so that the node in question can be inspected.

Details

The fields of the Pnode object correspond to the arguments of the calcDPCTable function. The output conditional probability table is then set in the node object in an implementation dependent way. Similarly, the current value of GetPriorWeight is used to set the weight that the prior table will be given in the EM algorithm.

Value

The node or net argument is returned invisibly. As a side effect the conditional probability table and prior weight of node (or a collection of nodes) is modified.

Logging and Debug Mode

As of version 0.6-2, the meaning of the debug argument is changed. In the new version, the flog.logger mechanism is used for progress reports, and error reporting. In particular, setting flog.threshold(DEBUG) (or TRACE) will cause progress reports to be sent to the logging output.

The debug argument has been repurposed. It now call recover when the error occurs, so that the problem can be debugged.

Note

The function BuildTable is an abstract generic function, and it needs a specific implementation. See the PNetica-package for an example.

Author(s)

Russell Almond

References

Almond, R. G. (2015) An IRT-based Parameterization for Conditional Probability Tables. Paper presented at the 2015 Bayesian Application Workshop at the Uncertainty in Artificial Intelligence Conference.

See Also

Pnode, PnodeProbs, PnodeQ, PnodePriorWeight, PnodeRules, PnodeLink, PnodeLnAlphas, PnodeAlphas, PnodeBetas, PnodeLinkScale,GetPriorWeight, calcDPCTable

In many implementations, it will be necessary to run PnetCompile after building the tables.

Examples

## Not run: 

## This is the implementation of BuildTable in Netica.  The [<- and
## NodeExperience functions are part of the RNetica implementation.

BuildTable.NeticaNode <- function (node) {
  node[] <- calcDPCFrame(ParentStates(node),PnodeStates(node),
                          PnodeLnAlphas(node), PnodeBetas(node),
                          PnodeRules(node),PnodeLink(node),
                          PnodeLinkScale(node),PnodeQ(node),
                          PnodeParentTvals(node))
  NodeExperience(node) <- GetPriorWeight(node)
  invisible(node)
}

## This is a simplified implementation of BuildAllTables
## (The full implementation adds logging and error handling.)
BuildAllTables <- function (net) {
  lapply(PnetPnodes(net),BuildTable)
  invisible(net)
}



## End(Not run)

ralmond/Peanut documentation built on Sept. 19, 2023, 8:27 a.m.