The FactorGraph supports the composite type
ContinuousTreeModel based on the forward–backward message passing schedule, with three fields:
ContinuousTreeGraph describes the tree factor graph obtained based on the input data. The GBP inference and marginal values are kept in the subtype
ContinuousInference. The system of the linear equations being solved is preserved in the subtype
ContinuousSystem. Note that the function
continuousTreeModel() returns the main composite type
ContinuousTreeModel with all subtypes.
Input arguments of the function
continuousTreeModel() describe the tree graphical model, while the function returns
Loads the system data passing arguments:
gbp = continuousTreeModel(coefficient, observation, variances)
continuousTreeModel() receives arguments by keyword to set the mean and variance of the virtual factor nodes to initiate messages from leaf variable nodes if the corresponding variable node does not have a singly connected factor node.
gbp = continuousTreeModel(DATA; mean = value, variance = value)
Default setting of the mean value is
mean = 0.0, while the default variance is equal to
variance = 1e10.
continuousTreeModel() receives argument by keyword to set the root variable node.
gbp = continuousTreeModel(DATA; root = index)
Default setting of the root variable node is
root = 1.
Function checks whether the factor graph has a tree structure.
tree = isTree(gbp)
The tree structure of tha factor graph is marked as
tree = true, the opposite is
tree = false. The function accepts the composite type
ContinuousTreeModel, as well as the type