 This version tells us that once we know the conditional probability distribution of each variable given its parents, \(p(X_{i}|\PA(X_{i}))\), we can compute the complete joint distribution over all of the variables. This captures Reichenbach’s idea that probabilistic correlations between events can ultimately be derived from probabilistic correlations resulting from cause and effect relationships.