. Usnrc, Guidance on the Treatment of Uncertainties Associated with PRAs in Risk- Informed Decision Making. NUREG-1855, 2009.

G. E. Apostolakis, The concept of probability in safety assessments of technological systems, Science, vol.250, issue.4986, pp.1359-1364, 1990.
DOI : 10.1126/science.2255906

J. C. Helton and W. Oberkampf, Alternative representations of epistemic uncertainty, Reliability Engineering & System Safety, vol.85, issue.1-3, pp.1-10, 2004.
DOI : 10.1016/j.ress.2004.03.001

P. Limbourg and E. De-rocquigny, Uncertainty analysis using evidence theory ??? confronting level-1 and level-2 approaches with data availability and computational constraints, Reliability Engineering & System Safety, vol.95, issue.5, pp.550-564, 2010.
DOI : 10.1016/j.ress.2010.01.005

G. E. Apostolakis and S. Kaplan, Pitfalls in risk calculations, Reliability Engineering, vol.2, issue.2, pp.135-145, 1981.
DOI : 10.1016/0143-8174(81)90019-6

D. Huang, T. Chen, and M. J. Wang, A fuzzy set approach for event tree analysis, Fuzzy Sets and Systems, vol.118, issue.1, pp.153-165, 2001.
DOI : 10.1016/S0165-0114(98)00288-7

T. Aven, On the Need for Restricting the Probabilistic Analysis in Risk Assessments to Variability, Risk Analysis, vol.14, issue.3, pp.354-360, 2010.
DOI : 10.1111/j.1539-6924.2009.01314.x

T. Aven, Interpretations of alternative uncertainty representations in a reliability and risk analysis context, Reliability Engineering & System Safety, vol.96, issue.3, pp.353-360, 2011.
DOI : 10.1016/j.ress.2010.11.004

T. Aven and R. Steen, The concept of ignorance in a risk assessment and risk management context, Reliability Engineering & System Safety, vol.95, issue.11, pp.1117-1122, 2010.
DOI : 10.1016/j.ress.2010.05.006

T. Aven and E. Zio, Some considerations on the treatment of uncertainties in risk assessment for practical decision making, Reliability Engineering & System Safety, vol.96, issue.1, pp.64-74, 2010.
DOI : 10.1016/j.ress.2010.06.001

S. Ferson, L. Ginzburg, and R. Akcakaya, Whereof one cannot speak: when input distributions are unknown, Risk Analysis, 1996.

G. J. Klir and B. Yuan, Fuzzy Sets and Fuzzy Logic: Theory and Applications, 1995.

S. Ferson, V. Kreinovich, L. Ginzburg, K. Sentz, and D. S. Myers, Constructing probability boxes and Dempster-Shafer structures, Sandia National Laboratories, 2003.

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, 2004.

J. C. Helton, J. D. Johnson, W. L. Oberkampf, and C. B. Storlie, A sampling-based computational strategy for the representation of epistemic uncertainty in model predictions with evidence theory, Computer Methods in Applied Mechanics and Engineering, vol.196, issue.37-40, pp.3980-98, 2007.
DOI : 10.1016/j.cma.2006.10.049

J. C. Helton, J. D. Johnson, W. L. Oberkampf, and C. J. Sallaberry, Representation of Analysis Results Involving Aleatory and Epistemic Uncertainty, Sandia National Laboratories, 2008.

K. Sentz and S. Ferson, Combination of Evidence in Dempster-Shafer Theory, Sandia National Laboratories, 2002.

C. Baudrit and D. Dubois, Practical representations of incomplete probabilistic knowledge, Computational Statistics & Data Analysis, vol.51, issue.1, pp.86-108, 2006.
DOI : 10.1016/j.csda.2006.02.009

C. Baudrit, D. Dubois, and D. Guyonnet, Joint Propagation and Exploitation of Probabilistic and Possibilistic Information in Risk Assessment, IEEE Transactions on Fuzzy Systems, vol.14, issue.5, pp.593-608, 2006.
DOI : 10.1109/TFUZZ.2006.876720

C. Baudrit, D. Dubois, and N. Perrot, Representing parametric probabilistic models tainted with imprecision, Fuzzy Sets and System, pp.1913-1928, 2008.

D. Dubois, Possibility theory and statistical reasoning, Computational Statistics & Data Analysis, vol.51, issue.1, pp.47-69, 2006.
DOI : 10.1016/j.csda.2006.04.015
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.324.6983

D. Dubois and H. Prade, Possibility Theory: An Approach to Computerized Processing of Uncertainty, 1988.

S. Ferson and J. G. Hajagos, Arithmetic with uncertain numbers: rigorous and (often) best possible answers, Reliability Engineering & System Safety, vol.85, issue.1-3, pp.135-152, 2004.
DOI : 10.1016/j.ress.2004.03.008

S. Ferson and W. T. Tucker, Sensitivity in risk analyses with uncertain numbers, 2006.

S. Ferson, V. Kreinovich, J. Hajagos, W. Oberkampf, and L. Ginzburg, Experimental Uncertainty Estimation and Statistics for Data Having Interval Uncertainty, 2007.

S. Ferson, P. Van-den-brink, T. L. Estes, K. Gallagher, R. O-'connor et al., Bounding Uncertainty Analyses, 2010.
DOI : 10.1201/EBK1439807347-c6

R. E. Moore, Methods and Applications of Interval Analysis, 1979.
DOI : 10.1137/1.9781611970906

.. A. Cullen and H. C. Frey, Probabilistic Techniques in Exposure Assessment: A Handbook for Dealing with Variability and Uncertainty in Models and Inputs, 1999.

K. D. Rao, H. S. Kushwaha, A. K. Verma, and A. Srividya, Quantification of epistemic and aleatory uncertainties in level-1 probabilistic safety assessment studies, Reliability Engineering & System Safety, vol.92, issue.7, pp.947-956, 2007.

M. H. Kalos and P. A. Whitlock, Monte Carlo methods, 1986.

M. Marseguerra and E. Zio, Basics of the Monte Carlo Method with Application to System Reliability, 2002.

P. Baraldi and E. Zio, A Combined Monte Carlo and Possibilistic Approach to Uncertainty Propagation in Event Tree Analysis, Risk Analysis, vol.8, issue.4, pp.1309-1326, 2008.
DOI : 10.1111/j.1539-6924.2008.01085.x

C. Baudrit, D. Guyonnet, and D. Dubois, Postprocessing the Hybrid Method for Addressing Uncertainty in Risk Assessments, Journal of Environmental Engineering, vol.131, issue.12, pp.1750-1754, 2005.
DOI : 10.1061/(ASCE)0733-9372(2005)131:12(1750)

C. Baudrit, D. Guyonnet, and D. Dubois, Joint propagation of variability and imprecision in assessing the risk of groundwater contamination, Journal of Contaminant Hydrology, vol.93, issue.1-4, pp.72-84, 2007.
DOI : 10.1016/j.jconhyd.2007.01.015

C. Baudrit, I. Couso, and D. Dubois, Joint propagation of probability and possibility in risk analysis: Towards a formal framework, International Journal of Approximate Reasoning, vol.45, issue.1, pp.82-105, 2007.
DOI : 10.1016/j.ijar.2006.07.001

J. A. Cooper, S. Ferson, and L. Ginzburg, Hybrid Processing of Stochastic and Subjective Uncertainty Data, Risk Analysis, vol.2, issue.4, pp.785-791, 1996.
DOI : 10.1016/0888-613X(90)90022-T

R. Flage, P. Baraldi, E. Zio, and T. Aven, Possibility-probability transformation in comparing different approaches to the treatment of epistemic uncertainties in a fault tree analysis, Reliability, Risk and Safety -Proceedings of the European Safety and Reliability (ESREL) 2010 Conference, pp.5-9, 2010.

D. Guyonnet, B. Bourgine, D. Dubois, H. Fargier, B. Côme et al., Hybrid Approach for Addressing Uncertainty in Risk Assessments, Journal of Environmental Engineering, vol.129, issue.1, pp.129-68, 2003.
DOI : 10.1061/(ASCE)0733-9372(2003)129:1(68)

E. Kentel and M. M. , Probabilistic-fuzzy health risk modeling, Stochastic Environmental Research and Risk Assessment, vol.18, issue.5, pp.324-338, 2004.
DOI : 10.1007/s00477-004-0187-3

E. Kentel and M. M. , Risk tolerance measure for decision-making in fuzzy analysis: a health risk assessment perspective, Stochastic Environmental Research and Risk Assessment, vol.1, issue.4, pp.405-417, 1965.
DOI : 10.1007/s00477-006-0073-2

E. Kentel and M. M. , 2D Monte Carlo versus 2D Fuzzy Monte Carlo health risk assessment, Stochastic Environmental Research and Risk Assessment, vol.19, issue.1, pp.86-96, 2005.
DOI : 10.1007/s00477-004-0209-1

B. Moller, Fuzzy randomness - a contribution to imprecise probability, ZAMM - Journal of Applied Mathematics and Mechanics / Zeitschrift f??r Angewandte Mathematik und Mechanik, vol.51, issue.6
DOI : 10.1002/zamm.200410153

B. Möller and M. Beer, Engineering computation under uncertainty ??? Capabilities of non-traditional models, Computers & Structures, vol.86, issue.10, pp.1024-1041, 2008.
DOI : 10.1016/j.compstruc.2007.05.041

B. Moller, W. Graf, and M. Beer, Safety assessment of structures in view of fuzzy randomness, Computers & Structures, vol.81, issue.15, pp.1567-1582, 2003.
DOI : 10.1016/S0045-7949(03)00147-0

B. Moller, M. Beer, W. Graf, and J. U. Sickert, Time-dependent reliability of textile-strengthened RC structures under consideration of fuzzy randomness, Computers & Structures, vol.84, issue.8-9, pp.585-603, 2006.
DOI : 10.1016/j.compstruc.2005.10.006

C. Baudrit and D. Dubois, Comparing Methods for Joint Objective and Subjective Uncertainty Propagation with an example in a risk assessment, Fourth International Symposium on Imprecise Probabilities and Their Applications (ISIPTA '05), 2005.

C. Baudrit, D. Dubois, and H. Fargier, Propagation of uncertainty involving imprecision and randomness, Proc. of the International Conference in Fuzzy Logic and Technology (EUSFLAT03), pp.653-658, 2003.

T. Fetz, Sets of joint probability measures generated by weighted marginal focal sets, Proceedings of the Second International Symposium on Imprecise Probability and Their Applications, pp.171-178, 2001.

T. Fetz and M. Oberguggenberger, Propagation of uncertainty through multivariate functions in the framework of sets of probability measures, Reliability Engineering & System Safety, vol.85, issue.1-3, pp.73-87, 2004.
DOI : 10.1016/j.ress.2004.03.004

J. C. Helton, J. D. Johnson, and W. L. Oberkampf, An exploration of alternative approaches to the representation of uncertainty in model predictions, Reliability Engineering & System Safety, vol.85, issue.1-3, pp.39-72, 2004.
DOI : 10.1016/j.ress.2004.03.025

S. Moral and N. Wilson, Importance sampling Monte-Carlo algorithms for the calculation of Dempster-Shafer belief Investigation of evidence theory for engineering applications, Proceedings of the 6th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU'96), pp.1337-1344, 1996.

W. L. Oberkampf, J. C. Helton, and K. Sentz, Mathematical Representation of Uncertainty, AIAA Non-Deterministic Approaches Forum Using random set theory to propagate epistemic uncertainty through a mechanical system, Reliability Engineering and System Safety, vol.85, pp.169-181, 2001.

F. Tonon, A. Bernardini, and A. Mammino, Determination of parameters range in rock engineering by means of Random Set Theory, Reliability Engineering & System Safety, vol.70, issue.3, pp.241-261, 2000.
DOI : 10.1016/S0951-8320(00)00058-2

F. Tonon, A. Bernardini, and A. Mammino, Reliability analysis of rock mass response by means of Random Set Theory, 63. S. Ferson, What Monte Carlo methods cannot do, Human and Environmental Risk Assessment, pp.263-282, 1996.
DOI : 10.1016/S0951-8320(00)00059-4

S. Ferson and M. A. Burman, Correlations, dependency bounds and extinction risks, Biological Conservation, vol.73, issue.2, pp.101-105, 1995.
DOI : 10.1016/0006-3207(95)90031-4

S. Ferson and L. R. Ginzburg, Different methods are needed to propagate ignorance and variability, Reliability Engineering & System Safety, vol.54, issue.2-3, pp.133-144, 1996.
DOI : 10.1016/S0951-8320(96)00071-3

S. Ferson and T. F. Long, Conservative uncertainty propagation in environmental risk assessments, in Environmental Toxicology and Risk Assessment, ASTM STP 1218, pp.97-110, 1994.

R. B. Nelsen, An Introduction to Copulas, Lecture Notes in Statistics, vol.139, 1999.
DOI : 10.1007/978-1-4757-3076-0

H. M. Regan, S. Ferson, and D. Berleant, Equivalence of five methods for bounding uncertainty Probabilistic arithmetic I: Numerical methods for calculating convolutions and dependency bounds, International Journal of Approximate Reasoning International Journal of Approximate Reasoning, vol.36, issue.4, pp.89-158, 1990.

I. Couso and S. , Independence concepts in evidence theory, International Journal of Approximate Reasoning, vol.51, issue.7, pp.748-758, 2010.
DOI : 10.1016/j.ijar.2010.02.004

I. Couso, S. Moral, and P. Walley, Examples of independence for imprecise probabilities, Proceedings of the First International Symposium on Imprecise Probability and Their ApplicationsImprecise Probabilities Project, pp.121-130, 1999.

I. Couso, S. Moral, and P. Walley, A survey of concepts of independence for imprecise probabilities, Risk Decision and Policy, vol.5, issue.2, pp.165-181, 2000.
DOI : 10.1017/S1357530900000156

J. Vejnarová, A Thorough Comparison of Two Conditional Independence Concepts for Belief Functions, Proceedings of Workshop on the Theory of Belief Functions, Workshop on the Theory of Belief Functions, 2010.

B. B. Yaghlane, P. Smets, and K. Mellouli, Belief function independence: I. The marginal case, International Journal of Approximate Reasoning, vol.29, issue.1, pp.47-70, 2002.
DOI : 10.1016/S0888-613X(01)00055-X

B. B. Yaghlane, P. Smets, and K. Mellouli, Belief function independence: II. The conditional case, International Journal of Approximate Reasoning, vol.31, issue.1-2, pp.31-75, 2002.
DOI : 10.1016/S0888-613X(02)00072-5

G. Coletti and B. Vantaggi, Possibility theory: Conditional independence, Fuzzy Sets and Systems, pp.1491-1513, 2006.

L. M. De-campos and J. F. Huete, Independence concepts in possibility theory: Part I, Fuzzy Sets and Systems, pp.127-152, 1999.

L. M. De-campos and J. F. Huete, Independence concepts in possibility theory: Part II, Fuzzy Sets and Systems, pp.487-505, 1999.

G. De-cooman, POSSIBILITY THEORY III: POSSIBILISTIC INDEPENDENCE, International Journal of General Systems, vol.25, issue.4, pp.353-371, 1997.
DOI : 10.1016/0165-0114(78)90029-5

E. Miranda and G. De-cooman, Epistemic independence in numerical possibility theory, International Journal of Approximate Reasoning, vol.32, issue.1, pp.23-42, 2003.
DOI : 10.1016/S0888-613X(02)00087-7

D. Dubois, H. Prade, and S. Sandri, On Possibility/Probability Transformations, pp.103-112, 1993.
DOI : 10.1007/978-94-011-2014-2_10
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.51.3212

P. Smets, Constructing the Pignistic Probability Function in a Context of Uncertainty, Uncertainty in Artificial Intelligence, vol.5, pp.29-39, 1990.
DOI : 10.1016/B978-0-444-88738-2.50010-5

D. Dubois, L. Foulloy, G. Mauris, and H. Prade, Probability-Possibility Transformations, Triangular Fuzzy Sets, and Probabilistic Inequalities, Reliable Computing, vol.10, issue.4, pp.273-297, 2004.
DOI : 10.1023/B:REOM.0000032115.22510.b5
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.104.6216

D. Dubois, H. Prade, and P. Smets, A definition of subjective possibility, International Journal of Approximate Reasoning, vol.48, issue.2, pp.352-364, 2008.
DOI : 10.1016/j.ijar.2007.01.005

D. Dubois and H. Prade, The mean value of a fuzzy number, Fuzzy Sets and Systems, vol.24, issue.3, pp.279-300, 1987.
DOI : 10.1016/0165-0114(87)90028-5

D. Ralescu, Average level of a fuzzy set, pp.119-126, 2002.
DOI : 10.1007/978-3-7908-1800-0_8

M. J. Frank, R. B. Nelsen, and B. Schweizer, Best-possible bounds for the distribution of a sum?a problem of Kolmogorov, Probability Theory and, pp.199-211, 1987.

S. Ferson, W. Root, and R. Kuhn, RAMAS Risk Calc: Risk Assessment with Uncertain Numbers, CM-113048 (EPRI, 1999.