belief.propagation: perform belief propagation.

In tavazzie/bnstructScore: Bayesian Network Structure Learning from Data with Missing Values

Description

Perform belief propagation for the network of an InferenceEngine, given a set of observations. In the current version of bnstruct, belief propagation can be computed only over a junction tree.

Usage

belief.propagation(ie, observations = NULL, return.potentials = FALSE)

## S4 method for signature 'InferenceEngine'
belief.propagation(ie, observations = NULL, return.potentials = FALSE)

Arguments

ie	an InferenceEngine object.
observations	list of observations, consisting in two vector, observed.vars for the observed variables, and observed.vals for the values taken by the variables listed in observed.vars. If no observations are provided, the InferenceEngine will use the ones it already contains.
return.potentials	if TRUE only the potentials are returned, instead of the default BN.

Value

updated InferenceEngine object.

Examples

## Not run: 
dataset <- BNDataset("file.header", "file.data")
bn <- BN(dataset)
ie <- InferenceEngine(bn)
ie <- belief.propagation(ie)

observations(ie) <- list("observed.vars"=("A","G","X"), "observed.vals"=c(1,2,1))
belief.propagation(ie)

## End(Not run)

tavazzie/bnstructScore documentation built on Dec. 23, 2021, 7:47 a.m.