Taboo Learning

Context

• Taboo Learning refers to structural learning that implements the so-called Taboo search for Bayesian networks.
• This method is particularly useful for refining networks built by human experts or for updating a network learned from a previous data set.

Usage

• Beyond taking into account the a priori knowledge represented by a given network and the specified equivalent number of cases, the starting point of Taboo is the current network — not the fully unconnected network (no arcs), as is the case for SopLEQ and Taboo Order.
• Furthermore, Fixed Arcs remain unchanged and Forbidden Arcs are taken into account.
• Temporal Indices are also taken into account.
• You can define the size of the Taboo list as well as the maximum number of parents and children. Unless thse options are checked, no constraints are used.
• Furthermore, you can specify whether to keep the existing network structure or not.