Charles.Wiecha@ISL1.RI.CMU.EDU.UUCP (01/27/87)
Influence Diagrams: Graphical Representations for Uncertainty
Ross D. Shachter
Department of Engineering-Economic Systems
Stanford University
Wednesday, January 28
2:30-4:00 PM
Porter Hall 223D
The influence diagram is a network for structuring bayesian decision analysis
problems. The nodes represent uncertain quantities, goals, and decisions, and
the arcs indicate probabilistic dependence and the observability of
information. The graphical heirarchy promotes discussion by emphasizing the
structure of a problem and the relationships among variables, while allowing
the details of assessment to be completed later. Because the components have a
basic mathematical interpretation, even a qualitative diagram has a precise
meaning. When the quantitative information is complete, the influence diagram
can be evaluated in a generalization of decision tree solving. Examples using
influence diagrams will be drawn from decision analysis, information theory,
dynamic programming, Kalman filtering, and expert systems. In the latter, we
ask the question "Why do probabilists insist on looking at everything
backwards?"