Exact Structure Learning of Bayesian Networks by Optimal Path Extension
  Bayesian networks are probabilistic graphical models often used in big data
analytics. The problem of exact structure learning is to find a network
structure that is optimal under certain scoring criteria. The problem is known
to be NP-hard and the existing methods are both computationally and memory
intensive. In this paper, we introduce a new approach for exact structure
learning. Our strategy is to leverage relationship between a partial network
structure and the remaining variables to constraint the number of ways in which
the partial network can be optimally extended. Via experimental results, we
show that the method provides up to three times improvement in runtime, and
orders of magnitude reduction in memory consumption over the current best
algorithms.
