Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Properties of Sensitivity Analysis of Bayesian Belief Networks
Annals of Mathematics and Artificial Intelligence
Building Probabilistic Networks: 'Where Do the Numbers Come From?' Guest Editors' Introduction
IEEE Transactions on Knowledge and Data Engineering
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
Bringing order into bayesian-network construction
Proceedings of the 3rd international conference on Knowledge capture
Evidence and scenario sensitivities in naive Bayesian classifiers
International Journal of Approximate Reasoning
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
When do numbers really matter?
Journal of Artificial Intelligence Research
A distance measure for bounding probabilistic belief change
International Journal of Approximate Reasoning
Making sensitivity analysis computationally efficient
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
Sensitivity analysis in discrete Bayesian networks
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Efficient sensitivity analysis in hidden markov models
International Journal of Approximate Reasoning
| Hi-index | 0.00 |
Upon varying parameters in a sensitivity analysis of a Bayesian network, the standard approach is to co-vary the parameters from the same conditional distribution such that their proportions remain the same. Alternative co-variation schemes are, however, possible. In this paper we investigate the properties of the standard proportional co-variation and introduce two alternative schemes: uniform and order-preserving co-variation. We theoretically investigate the effects of using alternative co-variation schemes on the so-called sensitivity function, and conclude that its general form remains the same under any linear co-variation scheme. In addition, we generalise the CD-distance for bounding global belief change to explicitly include the co-variation scheme under consideration. We prove a tight lower bound on this distance for parameter changes in single conditional probability tables.