
## References

Castillo, E., Guti´errez, J. M., & Hadi, A. S. (1997). Sensitivity analysis in discrete Bayesian networks. IEEE Transactions on Systems, Man, and Cybernetics, 27**, 412–423.**
Chan, H., & Darwiche, A. (2002). A distance measure for bounding probabilistic belief change. In **Proceedings of the Eighteenth National Conference on Artificial Intelligence** (AAAI)**, pp. 539–545.**
Coup´e, V. M. H., Peek, N., Ottenkamp, J., & Habbema, J. D. F. (1999). Using sensitivity analysis for efficient quantification of a belief network. **Artificial Intelligence in**
Medicine, 17**, 223–247.**
Darwiche, A. (2000). A differential approach to inference in Bayesian networks. In Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence (UAI)**, pp.**
123–132.

Greiner, R., Grove, A., & Schuurmans, D. (1997). Learning Bayesian nets that perform well. In **Proceedings of the 13th Conference on Uncertainty in Artificial Intelligence**
(UAI)**, pp. 198–207.**
Jensen, F. V., Lauritzen, S., & Olesen, K. (1990). Bayesian updating in recursive graphical models by local computation. Computational Statistics Quarterly, 4**, 269–282.**
Jensen, F. V. (1999). Gradient descent training of bayesian networks. In **Proceedings of the**
Fifth European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU)**, pp. 190–200.**
Jensen, F. V. (2001). Bayesian Networks and Decision Graphs**. Springer-Verlag, Inc., New**
York.

Kjærulff, U., & van der Gaag, L. C. (2000). Making sensitivity analysis computationally efficient. In **Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence**
(UAI)**, pp. 317–325.**
Laskey, K. B. (1995). Sensitivity analysis for probability **assessments in Bayesian networks.**
IEEE Transactions on Systems, Man, and Cybernetics, 25**, 901–909.**
Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference**. Morgan Kaufmann Publishers, Inc., San Mateo, California.**
Poole, D. (1998). Context-specific approximation in probabilistic inference. In **Proceedings**
of the 14th Conference on Uncertainty in Artificial Intelligence (UAI)**, pp. 447–454.**
Pradhan, M., Henrion, M., Provan, G., Del Favero, B., & Huang, K. (1996). The sensitivity of belief networks to imprecise probabilities: an experimental investigation. **Artificial**
Intelligence, 85**, 363–397.**
Russell, S., Binder, J., Koller, D., & Kanazawa, K. (1995). Local learning in probabilistic networks with hidden variables. In **Proceedings of the Fourteenth International Joint**
Conference on Artificial Intelligence (IJCAI)**, pp. 1146–1152.**
van der Gaag, L. C., & Renooij, S. (2001). Analysing sensitivity data from probabilistic networks. In **Proceedings of the 17th Conference on Uncertainty in Artificial Intelligence**
(UAI)**, pp. 530–537.**