Uncertainty epistemic

Epistemic uncertainty —missing knowledge—is due to a lack of information that through R D you could buy directly or estimate through proxy methods, if you so chose. These are controllable risks, although in practice they may be unduly expensive to control relative to the risk exposure (threat x likelihood). 

Apostolakis (1994, 1999) Aleatory uncertainty Epistemic uncertainty Uncertainty... 

Apostolakis GE. 1999. The distinction between aleatory and epistemic uncertainties is important an example from the inclusion of aging effects into probabihstic safety assessment. Proceedings of the PSA 99, August 22 to 25, 1999. Washington (DC) American Nuclear Society. 

Hora SC. 1996. Aleatory and epistemic uncertainty in probability elicitation with an example from hazardous waste management. Reliability Eng Syst Saf 54 217-223. 

Epistemic uncertainty The kind of uncertainty arising from imperfect knowledge. Epistemic uncertainty is also known as incertitnde, ignorance, subjective uncertainty. Type II or Type B uncertainty, redncible nncertainty, nonspeci-hcity and state-of-knowledge uncertainty. 

Robust Bayes A school of thought among Bayesian analysts in which epistemic uncertainty about prior distributions or likelihood functions is quantified and projected through Bayes rule to obtain a class of posterior distributions. 

All parameters mentioned are either stochastic or uncertain because of lack of knowledge. Hence, they either lead to aleatory or epistemic uncertainties in the calculations to be performed. That is why they are treated with probability distributions whose selection is indicated and justified below. 

This paper concentrates on the application of a Level I, at power and internal events, PRA, i.e. adopting the CDF as risk metric, to support the analysis of TS changes addressing epistemic uncertainties. 

Uncertainties can be categorized as either aleatory or epistemic uncertainties (Apostolakis, 1993). Aleatory uncertainty reflects our inability to predict random observable events. Epistemic uncertainty represents our confidence in the model and the numerical values of its parameters. This type ofuncertainty is also called state-of-knowledge uncertainty or just uncertainty (Zio and Apostolakis, 1996). 

Epistemic uncertainties can be roughly split into three categories and is done in RG 1.174. These are parameter, model, and completeness imcertainty. 

The epistemic uncertainty is that associated with the analyst s confidence in the predictions of the PRA model itself, and it reflects the analyst s assessment of how well the PRA model represents the actual system being modeled. 

A reasonable approach to addressing epistemic uncertainties in a quantitative way would consist of evolving from completeness to parametric uncertainties as far as possible, with an aim at allowing the comparison of PRA numerical results including uncertainty with the appropriate decision guidelines. 

Uncertainties that stem from lack of knowledge about different phenomena - epistemic uncertainty. This uncertainty resides from the lack of data to characterize the system or component failure, the lack of understanding and proper modeling of asset deterioration processes, the poor understanding of failure interdependencies in the system (physical or other phenomena) or the poor understanding of initiating events. 

The relationships between the IFs and the degradation states reached are not completely known they are obtained from information on literature models, whose parameters are often uncertain or not completely known, some scarce statistical data available and expert knowledge of qualitative nature. The model of the degradation process should be able to handle such epistemic uncertainties in this work, a fuzzy logic approach is proposed, in which the link between the IFs and the degradation states is described by means of Fuzzy Rule Bases (FRBs). 

In this Section, the Dempster-Shafer Theory (DST) of Evidence (Shafer, 1976) is considered for the representation of the epistemic uncertainty affecting the expert knowledge of the probability P Mi) that the alternative model Mi, I = 1,..., be correct. In the DS framework, a lower and an upper bound are introduced for representing the uncertainty associated to P (Ml). The lower bound, called behef, Bel (Mi), represents the amount of belief that directly supports M at least in part, whereas the upper bound, called plausibility, Pl Mi), measures the fact that M could be the correct model up to that value because there is only so much evidence that contradicts it. 

Handling epistemic uncertainties in fault tree analysis by probabilistic and possibilistic approaches... 

In engineering risk analysis a distinction is commonly made between aleatory (stochastic) and epistemic (knowledge) imcertainty see e.g. Apostolakis (1990) and Helton Burmaster (1996). Aleatory imcertainty refers to variation in populations epistemic uncertainty refers to lack of knowledge about phenomena, and usually translates into uncertainty about the parameters of the model used to describe the variation. Whereas epistemic uncertainty can be reduced, aleatory uncertainty cannot and for this reason it is sometimes called irreducible uncertainty. 

In the so-called probability of frequency approach (Kaplan Garrick 1981, Aven 2003), relative frequency-based probabilities are used to describe aleatory uncertainty and subjective probabilities to describe epistemic uncertainty. The probability of frequency approach differs fundamentally in philosophy but not much in practice fiwm a standard Bayesian approach (Aven 2003). In the Bayesian approach all uncertainty is epistemic, and probability is always considered an expression of belief it is not a property of the world in the way that a relative frequency-based probability is. The notion of aleatory uncertainty, sometimes just referred to as variation in the Bayesian approach (Aven 2003), is captured by the concept of chmce, defined as the limit of a relative frequency in an exchangeable, infinite Bernoulli series (Lind-ley 2006). A chance distribution is then the limit of... 

Specifically, it has been suggested that a possibilistic representation of epistemic uncertainty may be more adequate when sufficiently informative data are not available for statistical analysis and thus one has to resort mostly to information of qualitative namre. In particular, in cases where an expert does not have sufficiently refined knowledge or opinion to characterize the epistemic uncertainty in terms of probability distributions, possibility theory uses two measures of... 

There often seems to be a tacit assumption made that knowledge-based or subjective probability is an appropriate representation of epistemic uncertainty only when sufficient data exist, on which to base the probability or probability distribution in question. However, this is a misconception. Rather, the question is whether a comparison with the standard described in the previous section can be made. Faced with an expert/assessor who is not willing to specify a single number p or a single distribution G as his/her probability or probability distribution, respectively, but who will provide an interval or a set of distributions, an analyst may choose to define a possibUify distribution to reflect the imprecise input from the expert. Using a possibility distribution n implies that no particular probability distribution G is selected and assigned from among the family ( r) of probabihty distributions compatible with 7r. Compatible here means that for any interval... 

Helton, J.C. Burmaster, D.E. (1996) Guest editorial treatment of aleatory and epistemic uncertainty in performance assessments forcomplex systems. Reliability Engineering and System Safety 54 91-94. 

In order to make reliable decisions in design verifications of nuclear turbosets, it is important to evaluate the effect of epistemic uncertainties in the modeling on the dynamical behavior of the shaftline. The turbogenerator is a quite complex ensemble of substructures whose behavior is furthermore influenced by interacting with the supporting structure. 

Epistemic uncertainty during design phase possible modifications during design phase, inexact or imprecise information from the manufacturer. 

An additional imcertainty model is built to represent the epistemic uncertainty (level-2 or lack of knowledge) in the precise values of parameters 0. ... 

Namely, on average on X, the posterior variance is smaller than the prior variance. In other terms, the formula above states that posterior epistemic uncertainty decreases when adding data to prior knowledge. 

Two general sources contribute to the epistemic uncertainty on the predictions of the above-mentioned models. These are the so-called parameter or data uncertainty and the so-called model imcertainty. 

Fig. 1 shows the unavailability of the system function over time (saw-tooth curve) in comparison to the point estimate for the unavailabihty (dotted hne) derived from the mean unavailabilities of the system components. It can be seen that the point value commonly used for the unavailability of a system function (calculated from the mean and long-term unavailabilities of its components) is only a crude representation of the actually time-dependent unavailability. Obviously, it does not comply with the idea of conservativeness which is often claimed to justify the application of single values or simplified model assumptions in a PSA. In this context, it should be emphasized, that the variation considered for the unavailability of the system is only due to the stochastic behaviour of the system overtime. Epistemic uncertainties, forinstance, on the reliability parameters of system components were not considered. 

AT20 (°C) is the predicted shift in the Charpy transition temperature at the 41 Joules (30 ft-lb) energy level (the factors 1.1 and 0.99 in the equation account for the epistemic uncertainty associated with the sampled Charpy shift values)... 

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