  Conditional independence tests (CI tests) have received special attention
lately in Machine Learning and Computational Intelligence related literature as
an important indicator of the relationship among the variables used by their
models. In the field of Probabilistic Graphical Models (PGM)--which includes
Bayesian Networks (BN) models--CI tests are especially important for the task
of learning the PGM structure from data. In this paper, we propose the Full
Bayesian Significance Test (FBST) for tests of conditional independence for
discrete datasets. FBST is a powerful Bayesian test for precise hypothesis, as
an alternative to frequentist's significance tests (characterized by the
calculation of the \emph{p-value}).
