 In a Bayesian framework, probabilities are real numbers between 0 and 1 assigned to propositional variables A, B, C,…. These probabilities reflect an agent’s subjective credence, e.g., \(P(A)=0.6\) reflects that they think A is slightly more likely than not to be  true.[10]  At the heart of Bayesian Networks are the concepts of conditional probability and two variables being probabilistically independent. \(P(B \mid A)\) is the credence in B conditional on A being true and is defined as follows: