 Consider now \(\Pr(A&gt;C)\). Assume Stalnaker’s semantics for \(A&gt;C\); what does the probability of that conditional correspond to, if not the conditional probability? D. Lewis (1976) identifies a revision rule he calls imaging. Given \(\Pr\), he defines \(\Pr\mkern.1mu'_A\) to be the imaging under the antecedent, where \(\Pr\mkern.1mu'_A(w)=0\) if \(w\) is not an \(A\)-world; and if \(w\) is an \(A\)-world, then \(\Pr\mkern.1mu'_A(w)\) equals \(\Pr(w)\) augmented by the sum of the non-\(A\) worlds to which \(w\) comes closest according to Stalnaker’s selection function. Lewis shows that the probability of Stalnaker’s conditional is the probability of the consequent under imaging by the antecedent. Whereas conditioning upon \(A\) kills off the non-\(A\) worlds and spreads their mass uniformly over the \(A\)-worlds, imaging upon \(A\) is a different operation, which shifts the weight of non-\(A\) worlds non-uniformly to the \(A\)-worlds depending on the selection function (see  Fig. 4).