  The practice of stochastic sensitivity analysis described in the decision
analysis literature is a testimonial to the need for considering deviations
from precise point estimates of uncertainty. We propose the use of Bayesian
fuzzy probabilities within an influence diagram computational scheme for
performing sensitivity analysis during the solution of probabilistic inference
and decision problems. Unlike other parametric approaches, the proposed scheme
does not require resolving the problem for the varying probability point
estimates. We claim that the solution to fuzzy influence diagrams provides as
much information as the classical point estimate approach plus additional
information concerning stochastic sensitivity. An example based on diagnostic
decision making in microcomputer assembly is used to illustrate this idea. We
claim that the solution to fuzzy influence diagrams provides as much
information as the classical point estimate approach plus additional interval
information that is useful for stochastic sensitivity analysis.
