Properties of Sensitivity Analysis of Bayesian Belief Networks
Annals of Mathematics and Artificial Intelligence
Building Probabilistic Networks: 'Where Do the Numbers Come From?' Guest Editors' Introduction
IEEE Transactions on Knowledge and Data Engineering
A differential approach to inference in Bayesian networks
Journal of the ACM (JACM)
A distance measure for bounding probabilistic belief change
Eighteenth national conference on Artificial intelligence
Sensitivity analysis in Bayesian networks: from single to multiple parameters
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Evidence-invariant sensitivity bounds
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
A new characterization of probabilities in Bayesian networks
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
A probabilistic approach to resource allocation in distributed fusion systems
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
A Very Fast Learning Method for Neural Networks Based on Sensitivity Analysis
The Journal of Machine Learning Research
Evidence and scenario sensitivities in naive Bayesian classifiers
International Journal of Approximate Reasoning
Automatic diagnosis of mobile communication networks under imprecise parameters
Expert Systems with Applications: An International Journal
When do numbers really matter?
Journal of Artificial Intelligence Research
Sensitivity analysis in Markov networks
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
A distance measure for bounding probabilistic belief change
International Journal of Approximate Reasoning
Automatica (Journal of IFAC)
Modeling the correlations of crude oil properties based on sensitivity based linear learning method
Engineering Applications of Artificial Intelligence
A differential approach to inference in Bayesian networks
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
Making sensitivity analysis computationally efficient
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
When do numbers really matter?
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
Analysing sensitivity data from probabilistic networks
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
Efficient sensitivity analysis in hidden markov models
International Journal of Approximate Reasoning
Advances in Fuzzy Systems - Special issue on High Performance Fuzzy Systems for Real World Problems
International Journal of Approximate Reasoning
| Hi-index | 0.00 |
This paper presents an efficient computational method for performing sensitivity analysis in discrete Bayesian networks. The method exploits the structure of conditional probabilities of a target node given the evidence. First, the set of parameters which is relevant to the calculation of the conditional probabilities of the target node is identified. Next, this set is reduced by removing those combinations of the parameters which either contradict the available evidence or are incompatible. Finally, using the canonical components associated with the resulting subset of parameters, the desired conditional probabilities are obtained. In this way, an important saving in the calculations is achieved. The proposed method can also be used to compute exact upper and lower bounds for the conditional probabilities, hence a sensitivity analysis can be easily performed. Examples are used to illustrate the proposed methodology