Evidence-invariant Sensitivity Bounds
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.
Cite
@article{arxiv.1207.4170,
title = {Evidence-invariant Sensitivity Bounds},
author = {Silja Renooij and Linda C. van der Gaag},
journal= {arXiv preprint arXiv:1207.4170},
year = {2012}
}
Comments
Appears in Proceedings of the Twentieth Conference on Uncertainty in Artificial Intelligence (UAI2004)