Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
The sensitivity of belief networks to imprecise probabilities: an experimental investigation
Artificial Intelligence - Special volume on empirical methods
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
Analysing Sensitivity Data from Probabilistic Networks
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
A distance measure for bounding probabilistic belief change
Eighteenth national conference on Artificial intelligence
Local learning in probabilistic networks with hidden variables
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Context-specific approximation in probabilistic inference
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Learning Bayesian nets that perform well
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Sensitivity analysis in discrete Bayesian networks
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A differential approach to inference in Bayesian networks
Journal of the ACM (JACM)
Fitting and Compilation of Multiagent Models through Piecewise Linear Functions
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
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
Dynamic modeling of groundwater pollutants with bayesian networks
ICS'05 Proceedings of the 9th WSEAS International Conference on Systems
DYNAMIC MODELING OF GROUNDWATER POLLUTANTS WITH BAYESIAN NETWORKS
Applied Artificial Intelligence
Fixing the program my computer learned: barriers for end users, challenges for the machine
Proceedings of the 14th international conference on Intelligent user interfaces
A probabilistic plan recognition algorithm based on plan tree grammars
Artificial Intelligence
Sensitivity analysis in Markov networks
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Attaining monotonicity for Bayesian networks
ECSQARU'11 Proceedings of the 11th European conference on Symbolic and quantitative approaches to reasoning with uncertainty
Why-oriented end-user debugging of naive Bayes text classification
ACM Transactions on Interactive Intelligent Systems (TiiS)
From Reliability Block Diagrams to Fault Tree Circuits
Decision Analysis
Efficient sensitivity analysis in hidden markov models
International Journal of Approximate Reasoning
Bayesian network classifiers with reduced precision parameters
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
Impact of precision of Bayesian network parameters on accuracy of medical diagnostic systems
Artificial Intelligence in Medicine
International Journal of Approximate Reasoning
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Common wisdom has it that small distinctions in the probabilities (parameters) quantifying a belief network do not matter much for the results of probabilistic queries. Yet, one can develop realistic scenarios under which small variations in network parameters can lead to significant changes in computed queries. A pending theoretical question is then to analytically characterize parameter changes that do or do not matter. In this paper, we study the sensitivity of probabilistic queries to changes in network parameters and prove some tight bounds on the impact that such parameters can have on queries. Our analytic results pinpoint some interesting situations under which parameter changes do or do not matter. These results are important for knowledge engineers as they help them identify influential network parameters. They also help explain some of the previous experimental results and observations with regards to network robustness against parameter changes.