Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
An optimal approximation algorithm for Bayesian inference
Artificial Intelligence
Making Sensitivity Analysis Computationally Efficient
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
A Junction Tree Propagation Algorithm for Bayesian Networks with Second-Order Uncertainties
ICTAI '06 Proceedings of the 18th IEEE International Conference on Tools with Artificial Intelligence
Approximate algorithms for credal networks with binary variables
International Journal of Approximate Reasoning
The inferential complexity of Bayesian and credal networks
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Inference in polytrees with sets of probabilities
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Editorial: Bayesian networks in biomedicine and health-care
Artificial Intelligence in Medicine
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We present a new method to propagate lower bounds on conditional probability distributions in conventional Bayesian networks. Our method guarantees to provide outer approximations of the exact lower bounds. A key advantage is that we can use any available algorithms and tools for Bayesian networks in order to represent and infer lower bounds. This new method yields results that are provable exact for trees with binary variables, and results which are competitive to existing approximations in credal networks for all other network structures. Our method is not limited to a specific kind of network structure. Basically, it is also not restricted to a specific kind of inference, but we restrict our analysis to prognostic inference in this article. The computational complexity is superior to that of other existing approaches.