  We introduce a novel class of adjustment rules for a collection of beliefs.
This is an extension of Lewis' imaging to absorb probabilistic evidence in
generalized settings. Unlike standard tools for belief revision, our proposal
may be used when information is inconsistent with an agent's belief base. We
show that the functionals we introduce are based on the imaginary counterpart
of probability kinematics for standard belief revision, and prove that, under
certain conditions, all standard postulates for belief revision are satisfied.
