Abstract
In this paper we provide a general, unifying framework for probabilistic belief revision. We first introduce a probabilistic logic called p-logic that is
capable of representing and reasoning with basic
probabilistic information. With p-logic as the background logic, we define a revision function called
p-revision that resembles partial meet revision in
the AGM framework. We provide a representation
theorem for p-revision which shows that it can be
characterised by the set of basic AGM revision
postulates. P-revision represents an “all purpose”
method for revising probabilistic information that
can be used for, but not limited to, the revision
problems behind Bayesian conditionalisation, Jeffrey conditionalisation, and Lewis’s imaging. Importantly, p-revision subsumes the above three approaches indicating that Bayesian conditionalisation, Jeffrey conditionalisation, and Lewis’ imaging all obey the basic principles of AGM revision.
As well our investigation sheds light on the corresponding operation of AGM expansion in the probabilistic setting