We revisit the problem of revising probabilistic beliefs using uncertain evidence, and report results on four major issues relating to this problem: How to specify uncertain evidence? How to revise a distribution? Should, and do, iterated belief revisions commute? And how to provide guarantees on the amount of belief change induced by a revision? Our discussion is focused on two main methods for probabilistic revision: Jeffrey’s rule of probability kinematics and Pearl’s method of virtual evidence, where we analyze and unify these methods from the perspective of the questions posed above.