Sensitivity Analysis for Probability Assessments in Bayesian Networks

TL;DR

This paper introduces a methodology for analytically computing sensitivity values in Bayesian networks, aiding in model refinement and parameter estimation based on expert judgment.

Contribution

It presents a novel analytic approach for sensitivity analysis in Bayesian networks, facilitating improved knowledge elicitation and parameter optimization.

Findings

Sensitivity values effectively guide model adjustments.

Gradient descent algorithms improve parameter fitting.

Method enhances alignment with expert judgments.

Abstract

When eliciting probability models from experts, knowledge engineers may compare the results of the model with expert judgment on test scenarios, then adjust model parameters to bring the behavior of the model more in line with the expert's intuition. This paper presents a methodology for analytic computation of sensitivity values to measure the impact of small changes in a network parameter on a target probability value or distribution. These values can be used to guide knowledge elicitation. They can also be used in a gradient descent algorithm to estimate parameter values that maximize a measure of goodness-of-fit to both local and holistic probability assessments.