EQ

Context

EQ is an algorithm searching for the equivalence classes of Bayesian networks. This method is very efficient because it avoids local minima and and greatly reduces the size of the search space.
Like the Taboo learning algorithm, EQ can start with the current network.
Furthermore, Fixed Arcs are treated as normal arcs, while Forbidden Arcs are taken into account.
The Temporal Indices are also observed during learning.
In addition to standard learning options, you can choose to keep the current network structure when starting EQ learning.

P. Munteanu, M. Bendou, The EQ Framework for Learning Equivalence Classes of Bayesian Networks, First IEEE International Conference on Data Mining (IEEE ICDM), San Jose, November 2001.