Bayesian and non-Bayesian evidential updating
Artificial Intelligence
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Two views of belief: belief as generalized probability and belief as evidence
Artificial Intelligence
Use of the Gibbs sampler in expert systems
Artificial Intelligence
Decision-theoretic troubleshooting
Communications of the ACM
Introduction to Bayesian Networks
Introduction to Bayesian Networks
Four Results on Randomized Incremental Constructions
STACS '92 Proceedings of the 9th Annual Symposium on Theoretical Aspects of Computer Science
Decision making with interval influence diagrams
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Exploiting causal independence in Bayesian network inference
Journal of Artificial Intelligence Research
Local learning in probabilistic networks with hidden variables
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Conditions for the existence of belief functions corresponding to intervals of belief
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 1
Independence with lower and upper probabilities
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Propagation of 2-monotone lower probabilities on an undirected graph
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Bucket elimination: a unifying framework for probabilistic inference
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Theoretical foundations for abstraction-based probabilistic planning
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Geometric foundations for interval-based probabilities
Annals of Mathematics and Artificial Intelligence
On Two Pseudo-Paradoxes in Bayesian Analysis
Annals of Mathematics and Artificial Intelligence
Strong Conditional Independence for Credal Sets
Annals of Mathematics and Artificial Intelligence
Computing Intervals of Probabilities with Simulated Annealing and Probability Trees
ECSQARU '01 Proceedings of the 6th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Hill-climbing and branch-and-bound algorithms for exact and approximate inference in credal networks
International Journal of Approximate Reasoning
Graphical models for imprecise probabilities
International Journal of Approximate Reasoning
Inference in credal networks: branch-and-bound methods and the A/R+ algorithm
International Journal of Approximate Reasoning
Separation properties of sets of probability measures
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
Credal networks under maximum entropy
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
Irrelevance and independence relations in Quasi-Bayesian networks
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Using qualitative relationships for bounding probability distributions
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Inference in polytrees with sets of probabilities
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
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Robust Bayesian inference is the calculation of posterior probability bounds given perturbations in a probabilistic model. This paper focuses on perturbations that can be expressed locally in Bayesian networks through convex sets of distributions. Two approaches for combination of local models are considered. The first approach takes the largest set of joint distributions that is compatible with the local sets of distributions; we show how to reduce this type of robust inference to a linear programming problem. The second approach takes the convex hull of joint distributions generated from the local sets of distributions; we demonstrate how to apply interior-point optimization methods to generate posterior bounds and how to generate approximations that are guaranteed to converge to correct posterior bounds. We also discuss calculation of bounds for expected utilities and variances, and global perturbation models.