Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
A Differential Approach to Inference in Bayesian Networks
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Making Sensitivity Analysis Computationally Efficient
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
When do Numbers Really Matter?
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Sensitivity analysis in discrete Bayesian networks
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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
On the revision of probabilistic beliefs using uncertain evidence
Artificial Intelligence
Evidence and scenario sensitivities in naive Bayesian classifiers
International Journal of Approximate Reasoning
When do numbers really matter?
Journal of Artificial Intelligence Research
On the revision of probabilistic beliefs using uncertain evidence
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
On the revision of probabilistic beliefs using uncertain evidence
Artificial Intelligence
Reasoning about bayesian network classifiers
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
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We propose a distance measure between two probability distributions, which allows one to bound the amount of belief change that occurs when moving from one distribution to another. We contrast the proposed measure with some well known measures, including KL-divergence, showing how they fail to be the basis for bounding belief change as is done using the proposed measure. We then present two practical applications of the proposed distance measure: sensitivity analysis in belief networks and probabilistic belief revision. We show how the distance measure can be easily computed in these applications, and then use it to bound global belief changes that result from either the perturbation of local conditional beliefs or the accommodation of soft evidence. Finally, we show that two well known techniques in sensitivity analysis and belief revision correspond to the minimization of our proposed distance measure and, hence, can be shown to be optimal from that viewpoint.