Evidence-invariant Sensitivity Bounds

TL;DR

This paper introduces a method to determine sensitivity bounds in probabilistic networks that remain invariant regardless of the evidence entered, reducing the need for multiple analyses across different evidence scenarios.

Contribution

The paper presents a novel approach for establishing evidence-invariant sensitivity bounds, simplifying sensitivity analysis in probabilistic networks.

Findings

Method effectively identifies bounds where parameter variation does not change the most likely variable value.

Reduces computational effort by avoiding repeated analyses for different evidence scenarios.

Enhances understanding of parameter influence independent of specific evidence.

Abstract

The sensitivities revealed by a sensitivity analysis of a probabilistic network typically depend on the entered evidence. For a real-life network therefore, the analysis is performed a number of times, with different evidence. Although efficient algorithms for sensitivity analysis exist, a complete analysis is often infeasible because of the large range of possible combinations of observations. In this paper we present a method for studying sensitivities that are invariant to the evidence entered. Our method builds upon the idea of establishing bounds between which a parameter can be varied without ever inducing a change in the most likely value of a variable of interest.