A Transformational Characterization of Equivalent Bayesian Network
  Structures
  We present a simple characterization of equivalent Bayesian network
structures based on local transformations. The significance of the
characterization is twofold. First, we are able to easily prove several new
invariant properties of theoretical interest for equivalent structures. Second,
we use the characterization to derive an efficient algorithm that identifies
all of the compelled edges in a structure. Compelled edge identification is of
particular importance for learning Bayesian network structures from data
because these edges indicate causal relationships when certain assumptions
hold.
