Evidence-invariant sensitivity bounds

Authors:
Silja Renooij;Linda C. van der Gaag
Affiliations:
Utrecht University, TB Utrecht, The Netherlands;Utrecht University, TB Utrecht, The Netherlands
Venue:
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Year:
2004

Citing 6
Cited 3

Properties of Sensitivity Analysis of Bayesian Belief Networks

Annals of Mathematics and Artificial Intelligence
Making Sensitivity Analysis Computationally Efficient

UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Analysing Sensitivity Data from Probabilistic Networks

UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
A distance measure for bounding probabilistic belief change

Eighteenth national conference on Artificial intelligence
When do numbers really matter?

Journal of Artificial Intelligence Research
Sensitivity analysis in discrete Bayesian networks

IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans

Evidence and scenario sensitivities in naive Bayesian classifiers

International Journal of Approximate Reasoning
Efficient sensitivity analysis in hidden markov models

International Journal of Approximate Reasoning
Co-variation for sensitivity analysis in Bayesian networks: Properties, consequences and alternatives

International Journal of Approximate Reasoning

Quantified Score

Hi-index	0.00

Visualization

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.