  I propose a framework for an agent to change its probabilistic beliefs when a
new piece of propositional information $\alpha$ is observed. Traditionally,
belief change occurs by either a revision process or by an update process,
depending on whether the agent is informed with $\alpha$ in a static world or,
respectively, whether $\alpha$ is a 'signal' from the environment due to an
event occurring. Boutilier suggested a unified model of qualitative belief
change, which "combines aspects of revision and update, providing a more
realistic characterization of belief change." In this paper, I propose a
unified model of quantitative belief change, where an agent's beliefs are
represented as a probability distribution over possible worlds. As does
Boutilier, I take a dynamical systems perspective. The proposed approach is
evaluated against several rationality postulated, and some properties of the
approach are worked out.
