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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.


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