Benefits of Bayesian Network Models

Bibliography

1. [ALL 03] ALLEN D., DARWICHE A., “New advances in inference by recursive conditioning”, Proceedings of UAI, pp. 2–10, 2003.
2. [ANR 11] Structuring knowledge with object oriented Bayesian nets (SKOOB) project, Report no. ANR Project ’07 TLOG 021, ANR, French National Research Agency, 2011.
3. [ANS 94] ANSELL J., PHILLIPS M., Practical Methods for Reliability Data Analysis, Oxford University Press Inc., Oxford, 1994.
4. [ANT 11] ANTONUCCI A., “The Imprecise Noisy-OR Gate”, in Proceedings of the 14th International Conference on Information Fusion (FUSION 2011), Chicago, pp. 709–715, 2011.
5. [AVE 99] AVEN T., JENSEN U., Stochastic Models in Reliability, Applications of Mathematics, Springer-Verlag, New York, 1999.
6. [BAG 01] BAGDONAVICIUS V., NIKULIN M., “Estimation in degradation models with explanatory variables”, Lifetime Data Analysis, vol. 7, pp. 85–103, 2001.
7. [BEN 99] BENGIO Y., “Markovian models for sequential data”, Neural Computing Surveys, vol. 2, pp. 129–162, 1999.
8. [BEN 04] BEN SALEM A., BOUILLAUT L., AKNIN P. et al., “Dynamic Bayesian networks for classification of rail defects”, in IEEE 4th International Conference on Intelligent Systems Design and Applications, Budapest, Hungary, August 26–28 2004.
9. [BEN 06] BEN SALEM A., MULLER A., WEBER P., “Dynamic Bayesian networks in system reliability analysis”, in 6th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes, Beijing, P.R. China, pp. 481–486, August 30 2006.
10. [BEN 08] BEN SALEM A., Modèles ...