## Proceedings of an NEA Workshop on Uncertainty Analysis for Performance Assessments of Radioactive Waste Disposal Systems: Seattle, United States, 24-26 February 1987 |

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Page 63

A factorial design utilizes two or more fixed values (i.e.. levels) to represent each

variable under consideration. ... Sampling or Monte Carlo methods are based on

treating model input parameters as

A factorial design utilizes two or more fixed values (i.e.. levels) to represent each

variable under consideration. ... Sampling or Monte Carlo methods are based on

treating model input parameters as

**random variables**with assigned probability ...Page 241

In the case of a probabilistic code a decision is necessary on which parameter is

to be treated as constant and which as

calculation an actual value is assigned to each input parameter. For constant ...

In the case of a probabilistic code a decision is necessary on which parameter is

to be treated as constant and which as

**random variable**. At the beginning of eachcalculation an actual value is assigned to each input parameter. For constant ...

Page 243

Each vector contains a sampled set of actual values for all the

the sample being drawn with the LHS technique. The accepted input distributions

include histograms on linear or logarithmic scale, the (log)normal, and the beta ...

Each vector contains a sampled set of actual values for all the

**random variables**,the sample being drawn with the LHS technique. The accepted input distributions

include histograms on linear or logarithmic scale, the (log)normal, and the beta ...

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### Contents

EXECUTIVE SUMMARY CONCLUSIONS AND RECOMMENDATIONS | 11 |

OVERVIEW OF THE TREATMENT OF UNCERTAINTIES ARISING | 18 |

INTRODUCTORY ADDRESS | 32 |

Copyright | |

2 other sections not shown

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### Common terms and phrases

application approach Atomic Energy calculated Canada Limited Canadian concept ccdf components computer code conceptual model Conover correlation coefficients cumulative distribution function deterministic disposal system Energy of Canada errors example Figure fractile frequency histograms fuel waste disposal geologic geosphere groundwater Iman incertitudes input parameters input variables input vector Latin hypercube sampling mathematical model maximum dose model output model prediction multivariate normal distribution Nuclear Fuel Waste output variable p-value parameter uncertainty parameter values performance assessment probabilistic probabilistic risk assessments probability density functions procedure processes quantify quantitative radioactive waste radioactive waste disposal radiological radionuclide random variables rank correlation regression regulatory release repository risk assessment sampling method sampling strategy scenario selected sensitivity analysis simple random sampling sources of uncertainty specified standard deviation stochastic stratified sampling submodels system model SYVAC tests tion Type 2 uncertainty uncertainty analysis uncertainty associated UTAP variance variation vault Waste Management