In this paper we describe two approaches to the revision of probability
functions. We assume that a probabilistic state of belief is captured by
a counterfactual probability or Popper function, the revision of which
determines a new Popper function. We describe methods whereby the
original function determines the nature of the revised function. The
first is based on a probabilistic extension of Spohn's OCFs, whereas the
second exploits the structure implicit in the Popper function itself.
This stands in contrast with previous approaches that associate a
unique Popper function with each absolute (classical) probability
function. We also describe iterated revision using these models. Finally,
we consider the point of view that Popper functions may be abstract
representations of certain types of absolute probability functions, but
we show that our revision methods cannot be naturally interpreted as
conditionalization on these functions.