Equivalence Search in Learning Bayesian Networks

This project focuses on model-selection algorithms for learning Bayesian networks.
The impact that the search space connectivity has on the result is investigated
in some detail and general guidelines for defining efficient neighborhoods
that allows optimality are developed. These guidelines are formulated in
terms of local transformations on DAG Markov models, and are therefor directly
applicable to many model-selection algorithms that define their neighborhood
by local transformations. The quality of these guidelines has been demonstrated
in the development of a stochastic equivalence search (SES) algorithm and the
k-greedy equivalence search (KES) algorithm.