 The three probability tables above are all that is needed to define the probability distribution. The tables do not specify probabilistic dependencies between nodes that are not in a relation of child/parent, such as BT and \(W\). Since there is no arrow between them, nodes BT and \(W\) are assumed to be independent conditional on \(H\), that is, \(\Pr(W \pmid H)=\Pr(W \pmid H \wedge \BT)\). This fact represents, as part of the structure of the network, the independence between eyewitness testimony and blood evidence. A generalization of this fact is the so-called Markov condition (see the textbook by Neapolitan [2004] and the  supplement on Bayesian networks  of the entry on  artificial intelligence.)