 One explicit Bayesian account of unification as an epistemic, methodological virtue, has introduced the following measure of unity: a hypothesis \(h\) unifies phenomena \(p\) and \(q\) to the degree that given \(h, p\) is statistically/probabilistically relevant to (or correlated with) \(q\) (Myrvold 2003; a probabilistically equivalent measure of unity in Bayesian terms in McGrew 2003; on the equivalence, Schupbach 2005). This measure of unity has been criticized as neither necessary nor sufficient (Lange 2004; Lange’s criticism assumes the unification-explanation link; in a rebuttal, Schupbach has rejected this and other assumptions behind Lange’s criticism; Schupbach 2005).  In a recent development, Myrvold argues for mutual information unification, i.e., that hypotheses are said to be supported by their ability to increase the amount of what he calls the mutual information of the set of evidence statements; see Myrvold 2017. The explanatory unification contributed by hypotheses about common causes is an instance of the information condition.