Analysing Sensitivity Data from Probabilistic Networks

TL;DR

This paper explores methods for analyzing sensitivity data from probabilistic networks, focusing on derivatives of sensitivity functions and the concept of admissible deviation to interpret real-world network behavior.

Contribution

It introduces the use of derivatives and admissible deviation in sensitivity analysis, providing a new approach to interpret large sensitivity data sets in probabilistic networks.

Findings

Sensitivity analysis methods are effective for real-life networks.

Derivative-based analysis helps identify influential parameters.

Admissible deviation quantifies parameter robustness.

Abstract

With the advance of efficient analytical methods for sensitivity analysis ofprobabilistic networks, the interest in the sensitivities revealed by real-life networks is rekindled. As the amount of data resulting from a sensitivity analysis of even a moderately-sized network is alreadyoverwhelming, methods for extracting relevant information are called for. One such methodis to study the derivative of the sensitivity functions yielded for a network's parameters. We further propose to build upon the concept of admissible deviation, that is, the extent to which a parameter can deviate from the true value without inducing a change in the most likely outcome. We illustrate these concepts by means of a sensitivity analysis of a real-life probabilistic network in oncology.