Methods for the evaluation and synthesis of multiple sources of information applied to nuclear computer codes

This work is devoted to methods used to evaluate and synthesize information given by multiple sources about a variable which true value is not precisely known. We first recall probabilistic and possibilistic approaches to solve the problem. Each approach offers a formal setting to evaluate, synthesize and analyze information coming from multiple sources. They are then applied to the results of uncertainty studies performed in the framework of BEMUSE project.

[1]  Didier Dubois,et al.  Possibility Theory - An Approach to Computerized Processing of Uncertainty , 1988 .

[2]  BEMUSE Phase III Report: Uncertainty and Sensitivity Analysis of the LOFT L2-5 Test , 2007 .

[3]  François Delmotte,et al.  Detection of defective sources in the setting of possibility theory , 2007, Fuzzy Sets Syst..

[4]  Didier Dubois,et al.  Probability-Possibility Transformations, Triangular Fuzzy Sets, and Probabilistic Inequalities , 2004, Reliab. Comput..

[5]  Didier Dubois,et al.  Possibilistic information fusion using maximal coherent subsets , 2007, 2007 IEEE International Fuzzy Systems Conference.

[6]  H. W. Kalfsbeek,et al.  Elicitation, assessment, and pooling of expert judgments using possibility theory , 1995, IEEE Trans. Fuzzy Syst..

[7]  K. McConway.  Marginalization and Linear Opinion Pools , 1981 .

[8]  B. M. Brown,et al.  Practical Non-Parametric Statistics. , 1981 .

[9]  Robert L. Winkler,et al.  Combining Probability Distributions From Experts in Risk Analysis , 1999 .

[10]  Didier Dubois,et al.  Practical representations of incomplete probabilistic knowledge , 2006, Comput. Stat. Data Anal..

[11]  D. Dubois,et al.  When upper probabilities are possibility measures , 1992 .

[12]  L. Zadeh.  Fuzzy sets as a basis for a theory of possibility , 1999 .

[13]  B.C.P. Kraan,et al.  Probabilistic inversion in uncertainty analysis: And related topics , 2002 .

[14]  P. Walley.  Statistical Reasoning with Imprecise Probabilities , 1990 .

[15]  Roger M. Cooke,et al.  Probabilistic Risk Analysis: Probabilistic risk analysis , 2001 .

[16]  T. Bedford,et al.  Probabilistic Risk Analysis: Foundations and Methods , 2001 .

[17]  Isabelle Bloch.  Information combination operators for data fusion: a comparative review with classification , 1996, IEEE Trans. Syst. Man Cybern. Part A.

[18]  Yu. I. Lyubich.  Foundations and Methods , 1992 .

[19]  S. Ferson,et al.  Different methods are needed to propagate ignorance and variability , 1996 .

[20]  Didier Dubois,et al.  Possibility theory in information fusion , 2002, NMR.

[21]  T. Modis,et al.  Experts in uncertainty , 1993 .

[22]  Hichem Maaref,et al.  From adaptive to progressive combination of possibility distributions , 2003, Fuzzy Sets Syst..

[23]  Didier Dubois.  Fuzzy sets and systems , 1980 .