Inference

To exchange information over the tree factor graph, the FactorGraph provides forward–backward BP algorithm. We advise the reader to read the Section forward–backward message passing schedule which provides a detailed description of the inference algorithm.

Each of the inference functions accepts only the composite type DiscreteTreeModel, i.e., an output variable of the function bp = discreteTreeModel().

Forward inference

The set of functions that can be used to preform forward message inference:

forwardVariableFactor(bp)
forwardFactorVariable(bp)

Backward inference

The set of functions that can be used to preform backward message inference:

backwardVariableFactor(bp)
backwardFactorVariable(bp)

Marginal inference

To compute normalized marginals the FactorGraph provides the function:

marginal(bp)

To compute unnormalized marginals the FactorGraph provides the function:

marginalUnnormalized(bp)

Same as before, functions accept the composite type DiscreteTreeModel.