Decision Making Using Probabilistic Inference Methods
  The analysis of decision making under uncertainty is closely related to the
analysis of probabilistic inference. Indeed, much of the research into
efficient methods for probabilistic inference in expert systems has been
motivated by the fundamental normative arguments of decision theory. In this
paper we show how the developments underlying those efficient methods can be
applied immediately to decision problems. In addition to general approaches
which need know nothing about the actual probabilistic inference method, we
suggest some simple modifications to the clustering family of algorithms in
order to efficiently incorporate decision making capabilities.
