Sentence examples for models of uncertainty from inspiring English sources

However, there is an extensive body of literature focused on graded, or quantitative, models of uncertainty (Huber 2009; Halpern 2003).

Info-gap models of uncertainty are particularly useful when data on the uncertainties are quite limited.

It can be seen that when different models of uncertainty are used, the corrections of WT and PV output are different.

Here, we focus on non-probabilistic interval analysis models of uncertainty, which are adapted to the case of severe lack of information on uncertainty.

This paper presents a concept for formulating structural design-codes which is based on information-gap models of uncertainty rather than on probabilistic concepts.

In this paper non-probabilistic convex models of uncertainty are used to formulate reliability in terms of acceptable system performance given uncertain operating environment or uncertain geometrical imperfections.

Moreover, aspects of knowledge representation such as the modeling of uncertainty are discussed.

Unlike the modeling of uncertainty in stochastic programming, the uncertainty model in robust optimization is typically depicted as an uncertainty range.

For trading off between the two performance objectives, they formulate the problem as a mixed integer programming model with explicit probability modeling of uncertainty.

Stochastic analysis refers to the study and modeling of uncertainty with the objective of understanding its impact on performance, and is typically adopted in high risk projects.

Passing this prior parameter uncertainty through geostatistical simulation provides a realistic posterior model of uncertainty that accounts for the data configuration, conditioning, spatial correlation, and the domain limits.