Imagining Probabilistic Belief Change as Imaging (Technical Report)
  Imaging is a form of probabilistic belief change which could be employed for
both revision and update. In this paper, we propose a new framework for
probabilistic belief change based on imaging, called Expected Distance Imaging
(EDI). EDI is sufficiently general to define Bayesian conditioning and other
forms of imaging previously defined in the literature. We argue that, and
investigate how, EDI can be used for both revision and update. EDI's definition
depends crucially on a weight function whose properties are studied and whose
effect on belief change operations is analysed. Finally, four EDI
instantiations are proposed, two for revision and two for update, and
probabilistic rationality postulates are suggested for their analysis.
