Document: NRC Regulatory Guide
Document ID: 82659041-98b0-4721-b25d-c4fb2ea394d0
Document Type: regulatory_guide
Title: An Approach for Using Probabilistic Risk Assessment in Risk-Informed Decisions on Plant-Specific Changes to the Licensing Basis (Rev. 3)
Source: NRC Regulatory Guide Division 1
Source URL: https://www.nrc.gov/docs/ML1635/ML16358A153.pdf
Revision Date: 2023-06
Chapter: 
Section ID: RG-1.174
CFR Part: 
CFR Title: 

Content:
RA model itself. Because they are generally characterized and treated differently, it is useful to identify three classes of epistemic uncertainty that are addressed in and impact the results of PRAs: parameter uncertainty, model uncertainty, and completeness uncertainty. Completeness uncertainty can be regarded as one aspect of model uncertainty, but because of its importance, it is discussed separately. The following sections summarize the treatment of PRA uncertainty. NUREG-1855 describes the different types of uncertainty and provides acceptable guidance for the treatment of uncertainties in risk-informed decisionmaking. The bibliography may also be consulted for additional information. DG-1285, Page 32 2.5.2 Parameter Uncertainty Each of the models that are used, either to develop the PRA logic structure or to represent the basic events of that structure, has one or more parameters. Typically, each of these models (e.g., the Poisson model for initiating events) is assumed to be appropriate. However, the parameter values for these models are often not known perfectly. Parameter uncertainties are those associated with the values of the fundamental parameters of the PRA model, such as equipment failure rates, initiating event frequencies, and human error probabilities that are used in the quantification of the accident sequence frequencies. They are typically characterized by establishing probability distributions on the parameter values. These distributions can be interpreted as expressing the analyst’s degree of belief in the values these parameters could take, based on his or her state of knowledge and conditional on the underlying model being correct. It is straightforward and within the capability of most PRA codes to propagate the distribution representing uncertainty on the basic parameter values to generate a probability distribution on the results of the PRA (e.g., CDF, accident sequence frequencies, LERF). However, the analysis should be done to