Stochastic Sensitivity Analysis Using Fuzzy Influence Diagrams

TL;DR

This paper introduces a method using Bayesian fuzzy probabilities within influence diagrams to perform stochastic sensitivity analysis, providing richer information than traditional point estimates without re-solving the entire problem.

Contribution

It presents a novel approach that integrates fuzzy probabilities into influence diagrams for efficient sensitivity analysis during probabilistic inference and decision making.

Findings

Provides additional interval information for sensitivity analysis

Does not require re-solving the problem for different probability estimates

Demonstrated with a diagnostic decision-making example

Abstract

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.