Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Management Science
Positive Definiteness and Stability of Interval Matrices
SIAM Journal on Matrix Analysis and Applications
Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
Properties of Sensitivity Analysis of Bayesian Belief Networks
Annals of Mathematics and Artificial Intelligence
Sensitivity analysis: an aid for belief-network quantification
The Knowledge Engineering Review
Sensitivity analysis in Bayesian networks: from single to multiple parameters
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Extreme inaccuracies in Gaussian Bayesian networks
Journal of Multivariate Analysis
A Novel Bayes Model: Hidden Naive Bayes
IEEE Transactions on Knowledge and Data Engineering
Improving generalization of fuzzy IF-THEN rules by maximizing fuzzy entropy
IEEE Transactions on Fuzzy Systems
A distance measure for bounding probabilistic belief change
International Journal of Approximate Reasoning
Evaluating the difference between graph structures in Gaussian Bayesian networks
Expert Systems with Applications: An International Journal
Making sensitivity analysis computationally efficient
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
Information Sciences: an International Journal
Maximum Ambiguity-Based Sample Selection in Fuzzy Decision Tree Induction
IEEE Transactions on Knowledge and Data Engineering
Sensitivity to hyperprior parameters in Gaussian Bayesian networks
Journal of Multivariate Analysis
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In this work we study the effects of model inaccuracies on the description of a Gaussian Bayesian network with a set of variables of interest and a set of evidential variables. Using the Kullback-Leibler divergence measure, we compare the output of two different networks after evidence propagation: the original network, and a network with perturbations representing uncertainties in the quantitative parameters. We describe two methods for analyzing the sensitivity and robustness of a Gaussian Bayesian network on this basis. In the sensitivity analysis, different expressions are obtained depending on which set of parameters is considered inaccurate. This fact makes it possible to determine the set of parameters that most strongly disturbs the network output. If all of the divergences are small, we can conclude that the network output is insensitive to the proposed perturbations. The robustness analysis is similar, but considers all potential uncertainties jointly. It thus yields only one divergence, which can be used to confirm the overall sensitivity of the network. Some practical examples of this method are provided, including a complex, real-world problem.