17 – Treatment of uncertainty in performance assessments for the geological disposal of radioactive waste
暂无分享,去创建一个
[1] S. Kaplan,et al. On The Quantitative Definition of Risk , 1981 .
[2] R. Iman,et al. A distribution-free approach to inducing rank correlation among input variables , 1982 .
[3] R. Iman,et al. Rank correlation plots for use with correlated input variables , 1982 .
[4] R. Zeckhauser,et al. The perils of prudence: how conservative risk assessments distort regulation. , 1988, Regulatory toxicology and pharmacology : RTP.
[5] G. Apostolakis. The concept of probability in safety assessments of technological systems. , 1990, Science.
[6] Ralph L. Keeney,et al. Eliciting probabilities from experts in complex technical problems , 1991 .
[7] M. Thorne,et al. A review of expert judgment techniques with reference to nuclear safety , 1992 .
[8] Jon C. Helton,et al. Uncertainty and sensitivity analysis techniques for use in performance assessment for radioactive waste disposal , 1993 .
[9] Jon C. Helton,et al. Construction of complementary cumulative distribution functions for comparison with the EPA release limits for radioactive waste disposal , 1993 .
[10] M. C. Thorne,et al. The use of expert opinion in formulating conceptual models of underground disposal systems and the treatment of associated bias , 1993 .
[11] Jon C. Helton,et al. Calculation of reactor accident safety goals , 1993 .
[12] F. O. Hoffman,et al. Propagation of uncertainty in risk assessments: the need to distinguish between uncertainty due to lack of knowledge and uncertainty due to variability. , 1994, Risk analysis : an official publication of the Society for Risk Analysis.
[13] Jon C. Helton,et al. Treatment of Uncertainty in Performance Assessments for Complex Systems , 1994 .
[14] D. Hamby. A review of techniques for parameter sensitivity analysis of environmental models , 1994, Environmental monitoring and assessment.
[15] R L Sielken,et al. Challenges to default assumptions stimulate comprehensive realism as a new tier in quantitative cancer risk assessment. , 1995, Regulatory toxicology and pharmacology : RTP.
[16] Jon C. Helton,et al. Guest editorial: treatment of aleatory and epistemic uncertainty in performance assessments for complex systems , 1996 .
[17] M. Elisabeth Paté-Cornell,et al. Uncertainties in risk analysis: Six levels of treatment , 1996 .
[18] J. C. Helton,et al. Uncertainty and sensitivity analysis in the presence of stochastic and subjective uncertainty , 1997 .
[19] C. Allin Cornell,et al. Use of Technical Expert Panels: Applications to Probabilistic Seismic Hazard Analysis * , 1998 .
[20] H. Rabitz,et al. General foundations of high‐dimensional model representations , 1999 .
[21] Michael C. Cheok,et al. An approach for using risk assessment in risk-informed decisions on plant-specific changes to the licensing basis , 1999 .
[22] Jon C. Helton,et al. The 1996 performance assessment for the Waste Isolation Pilot Plant , 1998, Reliability Engineering & System Safety.
[23] Jon C. Helton,et al. Conceptual structure of the 1996 performance assessment for the Waste Isolation Pilot Plant , 2000, Reliab. Eng. Syst. Saf..
[24] Jon C. Helton,et al. Chapter 12 Mathematical and numerical approaches in performance assessment for radioactive waste disposal: dealing with uncertainty , 2000 .
[25] H. Rabitz,et al. High Dimensional Model Representations , 2001 .
[26] Timothy G. Trucano,et al. Verification and Validation in Computational Fluid Dynamics , 2002 .
[27] H Christopher Frey,et al. OF SENSITIVITY ANALYSIS , 2001 .
[28] Elisabeth Paté-Cornell,et al. Risk and Uncertainty Analysis in Government Safety Decisions , 2002, Risk analysis : an official publication of the Society for Risk Analysis.
[29] F. J. Davis,et al. Latin hypercube sampling and the propagation of uncertainty in analyses of complex systems , 2002, Reliab. Eng. Syst. Saf..
[30] T. Trucano,et al. Verification, Validation, and Predictive Capability in Computational Engineering and Physics , 2004 .
[31] D. Cacuci,et al. A Comparative Review of Sensitivity and Uncertainty Analysis of Large-Scale Systems—II: Statistical Methods , 2004 .
[32] George J. Klir,et al. Generalized information theory: aims, results, and open problems , 2004, Reliab. Eng. Syst. Saf..
[33] J. Tinsley Oden,et al. Verification and validation in computational engineering and science: basic concepts , 2004 .
[34] P. Roache. Building PDE codes to be verifiable and validatable , 2004, Computing in Science & Engineering.
[35] R. Cooke,et al. Expert judgement elicitation for risk assessments of critical infrastructures , 2004 .
[36] Jon C. Helton,et al. An exploration of alternative approaches to the representation of uncertainty in model predictions , 2003, Reliab. Eng. Syst. Saf..
[37] A. O'Hagan,et al. Statistical Methods for Eliciting Probability Distributions , 2005 .
[38] A. Saltelli,et al. Sensitivity analysis for chemical models. , 2005, Chemical reviews.
[39] Laura Painton Swiler,et al. Calibration, validation, and sensitivity analysis: What's what , 2006, Reliab. Eng. Syst. Saf..
[40] Jon C. Helton,et al. Survey of sampling-based methods for uncertainty and sensitivity analysis , 2006, Reliab. Eng. Syst. Saf..
[41] Jay D. Johnson,et al. A sampling-based computational strategy for the representation of epistemic uncertainty in model predictions with evidence theory , 2007 .
[42] Jon C. Helton,et al. Multiple predictor smoothing methods for sensitivity analysis: Example results , 2008, Reliab. Eng. Syst. Saf..
[43] Jon C. Helton,et al. Multiple predictor smoothing methods for sensitivity analysis: Description of techniques , 2008, Reliab. Eng. Syst. Saf..
[44] Jon C. Helton,et al. Conceptual basis for the definition and calculation of expected dose in performance assessments for the proposed high-level radioactive waste repository at Yucca Mountain, Nevada , 2009, Reliab. Eng. Syst. Saf..