Sensitivity analysis in Bayesian networks: from single to multiple parameters

Authors:
Hei Chan;Adnan Darwiche
Affiliations:
University of California, Los Angeles, CA;University of California, Los Angeles, CA
Venue:
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Year:
2004

Citing 9
Cited 10

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
User Interface Tools for Navigation in Conditional Probability Tables and Elicitation of Probabilities 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
A differential approach to inference in Bayesian networks

Journal of the ACM (JACM)
A distance measure for bounding probabilistic belief change

Eighteenth national conference on Artificial intelligence
When do numbers really matter?

Journal of Artificial Intelligence Research
A logic for reasoning about evidence

UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Sensitivity analysis in discrete Bayesian networks

IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans

On probabilistic inference by weighted model counting

Artificial Intelligence
Sensitivity analysis in Markov networks

IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Publishing naive Bayesian classifiers: privacy without accuracy loss

Proceedings of the VLDB Endowment
Why-oriented end-user debugging of naive Bayes text classification

ACM Transactions on Interactive Intelligent Systems (TiiS)
A Study of Interactions in the Risk Assessment of Complex Engineering Systems: An Application to Space PSA

Operations Research
From Reliability Block Diagrams to Fault Tree Circuits

Decision Analysis
Efficient sensitivity analysis in hidden markov models

International Journal of Approximate Reasoning
The effect of block parameter perturbations in Gaussian Bayesian networks: Sensitivity and robustness

Information Sciences: an International Journal
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
Co-variation for sensitivity analysis in Bayesian networks: Properties, consequences and alternatives

International Journal of Approximate Reasoning

Quantified Score

Hi-index	0.00

Visualization

Abstract

Previous work on sensitivity analysis in Bayesian networks has focused on single parameters, where the goal is to understand the sensitivity of queries to single parameter changes, and to identify single parameter changes that would enforce a certain query constraint. In this paper, we expand the work to multiple parameters which may be in the CPT of a single variable, or the CPTs of multiple variables. Not only do we identify the solution space of multiple parameter changes that would be needed to enforce a query constraint, but we also show how to find the optimal solution, that is, the one which disturbs the current probability distribution the least (with respect to a specific measure of disturbance). We characterize the computational complexity of our new techniques and discuss their applications to developing and debugging Bayesian networks, and to the problem of reasoning about the value (reliability) of new information.