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
Analysing Sensitivity Data from Probabilistic Networks
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
On the revision of probabilistic beliefs using uncertain evidence
Artificial Intelligence
Information Affinity: A New Similarity Measure for Possibilistic Uncertain Information
ECSQARU '07 Proceedings of the 9th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
A General Model for Epistemic State Revision using Plausibility Measures
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Sensitivity analysis in Markov networks
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
On the revision of probabilistic beliefs using uncertain evidence
Artificial Intelligence
Evaluating the difference between graph structures in Gaussian Bayesian networks
Expert Systems with Applications: An International Journal
Attaining monotonicity for Bayesian networks
ECSQARU'11 Proceedings of the 11th European conference on Symbolic and quantitative approaches to reasoning with uncertainty
Towards a definition of evaluation criteria for probabilistic classifiers
ECSQARU'05 Proceedings of the 8th European conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Efficient sensitivity analysis in hidden markov models
International Journal of Approximate Reasoning
Information Sciences: an International Journal
International Journal of Approximate Reasoning
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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 some theoretical properties on its ability to bound belief changes. 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.