 Jeffrey conditionalization is flexible enough to accommodate the Mudrunner Case. Suppose that the immediate effect of the gambler’s sky-looking experience is to raise the credence in \(E\), i.e. \(\Cr(\mathsf{muddy})\). One feature of Jeffrey conditionalization is that, since certain credence ratios are required to be held constant, one has to hold constant the conditional credences given \(E\) and also those given \(\neg E\), such as \(\Cr(\mathsf{win} \mid \mathsf{muddy})\) and \(\Cr(\mathsf{win} \mid \neg\mathsf{muddy})\). The credences mentioned above can be used to express \(\Cr(\mathsf{win})\) as follows (thanks to Probabilism and the Ratio Formula):