D. Marquez, M. Neil, and N. Fenton, Improved reliability modeling using Bayesian networks and dynamic discretization, Reliability Engineering & System Safety, vol.95, issue.4, pp.412-425, 2010.
DOI : 10.1016/j.ress.2009.11.012

O. Doguc and J. E. Ramirez-marquez, A generic method for estimating system reliability using Bayesian networks, Reliability Engineering & System Safety, vol.94, issue.2, pp.542-550, 2009.
DOI : 10.1016/j.ress.2008.06.009

L. Portinale, D. C. Raiteri, and S. Montani, Supporting reliability engineers in exploiting the power of Dynamic Bayesian Networks, International Journal of Approximate Reasoning, vol.51, issue.2, pp.179-195, 2010.
DOI : 10.1016/j.ijar.2009.05.009

M. Neil and D. Marquez, Availability modelling of repairable systems using Bayesian networks, Engineering Applications of Artificial Intelligence, vol.25, issue.4, pp.698-704, 2012.
DOI : 10.1016/j.engappai.2010.06.003

A. Antonucci, The imprecise noisy-or gate, Proc. 14th International Conference on Information Fusion, pp.1-7, 2011.

S. Srinivas, A Generalization of the Noisy-Or Model, Proc. 9th International Conference on Uncertainty in Artificial Intelligence, pp.208-215, 1993.
DOI : 10.1016/B978-1-4832-1451-1.50030-5

G. Fallet, P. Weber, C. Simon, B. Iung, and C. Duval, Evidential networkbased extension of leaky noisy-or structure for supporting risks analyses, Proc. 8th International Symposium SAFEPROCESS, 2012, p.0
URL : https://hal.archives-ouvertes.fr/hal-00720902

G. Shafer, A mathematical theory of evidence, 1976.

T. Denoeux, A k-nearest neighbor classification rule based on Dempster-Shafer theory, IEEE Transactions on Systems, Man, and Cybernetics, vol.25, issue.5, pp.804-813, 1995.
DOI : 10.1109/21.376493

Z. Liu, Q. Pan, J. Dezert, and G. Mercier, Credal classification rule for uncertain data based on belief functions, Pattern Recognition, vol.47, issue.7, pp.2532-2541, 2014.
DOI : 10.1016/j.patcog.2014.01.011
URL : https://hal.archives-ouvertes.fr/hal-01060091

Z. Liu, Q. Pan, G. Mercier, and J. Dezert, A New Incomplete Pattern Classification Method Based on Evidential Reasoning, IEEE Transactions on Cybernetics, vol.45, issue.4, pp.635-646, 2015.
DOI : 10.1109/TCYB.2014.2332037
URL : https://hal.archives-ouvertes.fr/hal-01162207

M. Masson and T. Denoeux, ECM: An evidential version of the fuzzy c-means algorithm, Pattern Recognition, vol.41, issue.4, pp.1384-1397, 2008.
DOI : 10.1016/j.patcog.2007.08.014

T. Denoeux and M. Masson, EVCLUS: Evidential Clustering of Proximity Data, IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), vol.34, issue.1, pp.95-109, 2004.
DOI : 10.1109/TSMCB.2002.806496

K. Zhou, A. Martin, Q. Pan, and Z. Liu, Evidential relational clustering using medoids, Proc. 18th International Conference on Information Fusion (Fusion, pp.413-420, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01176143

K. Zhou, A. Martin, Q. Pan, and Z. Liu, ECMdd: Evidential c -medoids clustering with multiple prototypes, Pattern Recognition, vol.60, 2016.
DOI : 10.1016/j.patcog.2016.05.005
URL : https://hal.archives-ouvertes.fr/hal-01326332

D. Wei, X. Deng, X. Zhang, Y. Deng, and S. Mahadevan, Identifying influential nodes in weighted networks based on evidence theory, Physica A: Statistical Mechanics and its Applications, vol.392, issue.10, pp.2564-2575, 2013.
DOI : 10.1016/j.physa.2013.01.054

K. Zhou, A. Martin, and Q. Pan, Evidential Communities for Complex Networks, Proc. 15th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, pp.557-566, 2014.
DOI : 10.1007/978-3-319-08795-5_57
URL : https://hal.archives-ouvertes.fr/hal-01101172

K. Zhou, A. Martin, Q. Pan, and Z. Liu, Median evidential c-means algorithm and its application to community detection, Knowledge-Based Systems, vol.74, pp.69-88, 2015.
DOI : 10.1016/j.knosys.2014.11.010
URL : https://hal.archives-ouvertes.fr/hal-01100902

K. Zhou, A. Martin, and Q. Pan, A similarity-based community detection method with multiple prototype representation, Physica A: Statistical Mechanics and its Applications, vol.438, pp.519-531, 2015.
DOI : 10.1016/j.physa.2015.07.016
URL : https://hal.archives-ouvertes.fr/hal-01185866

T. Denoeux, Maximum Likelihood Estimation from Uncertain Data in the Belief Function Framework, IEEE Transactions on Knowledge and Data Engineering, vol.25, issue.1, pp.119-130
DOI : 10.1109/TKDE.2011.201
URL : https://hal.archives-ouvertes.fr/hal-00804343

E. Côme, L. Oukhellou, T. Denoeux, and P. Aknin, Learning from partially supervised data using mixture models and belief functions, Pattern Recognition, vol.42, issue.3, pp.334-348, 2009.
DOI : 10.1016/j.patcog.2008.07.014

K. Zhou, A. Martin, and Q. Pan, Evidential-EM Algorithm Applied to Progressively Censored Observations, Proc. 15th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, pp.180-189, 2014.
DOI : 10.1007/978-3-319-08852-5_19
URL : https://hal.archives-ouvertes.fr/hal-01100840

C. Simon, P. Weber, and A. Evsukoff, Bayesian networks inference algorithm to implement Dempster Shafer theory in reliability analysis, Reliability Engineering & System Safety, vol.93, issue.7, pp.950-963, 2008.
DOI : 10.1016/j.ress.2007.03.012
URL : https://hal.archives-ouvertes.fr/hal-00139492

C. Simon and P. Weber, Evidential Networks for Reliability Analysis and Performance Evaluation of Systems With Imprecise Knowledge, IEEE Transactions on Reliability, vol.58, issue.1, pp.69-87, 2009.
DOI : 10.1109/TR.2008.2011868
URL : https://hal.archives-ouvertes.fr/hal-00340040

B. B. Yaghlane and K. Mellouli, Inference in directed evidential networks based on the transferable belief model, International Journal of Approximate Reasoning, vol.48, issue.2, pp.399-418, 2008.
DOI : 10.1016/j.ijar.2008.01.002

P. Smets and R. Kennes, The transferable belief model, Artificial Intelligence, vol.66, issue.2, pp.191-234, 1994.
DOI : 10.1016/0004-3702(94)90026-4
URL : https://hal.archives-ouvertes.fr/hal-01185821

D. Heckerman and J. S. Breese, Causal independence for probability assessment and inference using Bayesian networks, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, vol.26, issue.6, pp.826-831, 1996.
DOI : 10.1109/3468.541341

F. G. Cozman, Axiomatizing noisy-or, Proc. 16th European Conference on Artificial Intelligence, pp.979-980, 2004.

Z. Lian-wei and G. Hai-peng, Introduction to bayesian networks, 2006.

P. Smets, The application of the matrix calculus to belief functions, International Journal of Approximate Reasoning, vol.31, issue.1-2, pp.1-30, 2002.
DOI : 10.1016/S0888-613X(02)00066-X

S. Ferson, R. B. Nelsen, J. Hajagos, D. J. Berleant, J. Zhang et al., Dependence in probabilistic modeling, Dempster-Shafer theory, and probability bounds analysis, Citeseer, vol.3072, 2004.

P. Walley, Statistical reasoning with imprecise probabilities, 1991.
DOI : 10.1007/978-1-4899-3472-7

L. V. Utkin and F. P. Coolen, Imprecise reliability: An introductory overview, Computational intelligence in reliability engineering, 2007.
DOI : 10.1007/978-3-540-37372-8_10

P. Smets, Decision making in the TBM: the necessity of the pignistic transformation, International Journal of Approximate Reasoning, vol.38, issue.2, pp.133-147, 2005.
DOI : 10.1016/j.ijar.2004.05.003

P. Smets, Belief functions on real numbers, International Journal of Approximate Reasoning, vol.40, issue.3, pp.181-223, 2005.
DOI : 10.1016/j.ijar.2005.04.001

D. Mercier, G. Cron, T. Denoeux, and M. Masson, Fusion of multi-level decision systems using the transferable belief model, 2005 7th International Conference on Information Fusion, pp.885-892, 2005.
DOI : 10.1109/ICIF.2005.1591952