BayesianMachineLearningProbabilityTheoryStatistics Given random variables, , , and on some underlying probability space then is conditionally independent of given , or if Different configurations of a Probabilistic Graphical Model determine various conditional independence relations, which is called D-Separation in Bayesian Networks or just Separation in undirected models like Markov Random Fields.