DERIVING PARAMETER PROBABILITY DENSITY-FUNCTIONS

The long-term performance of a nuclear fuel waste disposal system is typically studied by modelling potential releases of contaminants to the environment and the consequent health risk to humans. To deal with uncertainty in the estimated consequences of the releases, the behaviour of the disposal system may be repeatedly simulated under the direction of a probabilistic assessment code. In each simulation, parameters in the system model take different values to reflect the uncertainty about their values in the real system. The selection of the possible values of the parameters is governed by a probability density function (PDF) for each parameter. This paper examines the question of how to derive parameter PDFs for such assessments. The paper includes: 1. A definition of the concept of probability, and the mathematical properties of PDFs and the related cumulative distribution functions (CDFs). 2. A discussion of the influence of the context in which PDFs are derived on their form and interpretation. Key elements of the context include what is known about the disposal system, the structure and extent of the model description of the system, and how the results of the assessment will be used. 3. A description of approaches to deriving parameter PDFs. In cases where data are unavailable or incomplete, reliance must be placed on the judgement of experts to generate subjective probability distributions. Examples are given of PDFs defined for recent Canadian assessments. The paper concludes with remarks on managing reliably the large body of PDF data for an assessment, how values are sampled from PDFs, and desirable future developments.