discreteTreeInference.md

[Inference](@id inferenceTreeDiscrete)

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] (@ref treeSchedule) 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().

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Forward inference

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

forwardVariableFactor(bp)
forwardFactorVariable(bp)

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Backward inference

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

backwardVariableFactor(bp)
backwardFactorVariable(bp)

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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.