Structural Constraints

The Learning Wizard allows you to specify available knowledge about dependences or independences among pairs of variables in the data set. That is, if a pair of variables are known to be marginally dependent (i.e., a there must be a link between them) or conditionally independent (i.e., there must not be a link between them), such knowledge can be specified by imposing structural constraints upon the graphical model learned from the data. You can specify four different kinds of constraints:

Please note that only one constraint can be specified per pair of variables.

Saving / Importing Information

If the learning process is to be repeated a number of time, it can be rather cumbersome to specify the same constraints over and over again. To avoid this, the model information, including the constraints, can be saved to a net-file using the “Save”-button :

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This allow for later import of the saved information by using the “Import”-button : .

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Note, that this allows for import of all the model information, such as node positions, labels, sizes, etc. This can be very useful, if the data relates to a network whose structure is known. In that case, you can simply import the labels and positions of the nodes. The learned network can then easily be compared to the existing one.