In this work, we investigate the issue of providing a statistical model for the response of a computer model-described nuclear engineering system, for use in uncertainty propagation. The motivation behind our approach is the need for providing an uncertainty assessment even in the circumstances where only a few samples are available. Building on our recent work in using a regression approach wi...

abstarct development of greenhouse gases in future periods not only causes change in average amounts of climate variables but also makes variables of this variability affected. then for sure concerning alternations followed with climate variables’ fluctuations and its average amounts in effecting on runoff would make more reliable results. in this inquiry initially fluctuations and average amou...

In their paper, Watson and Holmes (2015) follow the statistical decision approach pioneered by Wald (1950). Under Wald’s perspective, the aim of the analysis shifts from the discovery of a statistical “truth” (for instance, as revealed by the correct statistical model), to making decisions that are defensible according to posited objective functions that trade off alternative aims. We also draw...

Background: The purpose of this study is to prevent the prevalence of infectious diseases in vulnerable groups by anticipating the role of actors in implementing decision-making models in conditions of uncertainty in medical universities. Methods: This research is an applied research by combining qualitative and quantitative methods based on the foundation data theory (Grand Theory). To determ...

Monte Carlo dose calculations will potentially reduce systematic errors that may be present in currently used dose calculation algorithms. However, Monte Carlo calculations inherently contain random errors, or statistical uncertainty, the level of which decreases inversely with the square root of computation time. Our purpose in this study was to determine the level of uncertainty at which a lu...

In this paper we demonstrate the only available scalable information bounds for quantities of interest of high dimensional probabilistic models. Scalability of inequalities allows us to (a) obtain uncertainty quantification bounds for quantities of interest in the large degree of freedom limit and/or at long time regimes; (b) assess the impact of large model perturbations as in nonlinear respon...

We couple a species range limit hypothesis with the output of an ensemble of general circulation models to project the poleward range limit of gray snapper. Using laboratory-derived thermal limits and statistical downscaling from IPCC AR4 general circulation models, we project that gray snapper will shift northwards; the magnitude of this shift is dependent on the magnitude of climate change. W...

In many engineering applications, we have to combine probabilistic and interval uncertainty. For example, in environmental analysis, we observe a pollution level x(t) in a lake at different moments of time t, and we would like to estimate standard statistical characteristics such as mean, variance, autocorrelation, correlation with other measurements. In environmental measurements, we often onl...

We review uncertainty quantification (UQ) for hyperbolic systems of conservation (balance) laws. The input uncertainty could be in the initial data, fluxes, coefficients, source terms or boundary conditions. We focus on forward UQ or uncertainty propagation and review deterministic methods such as stochastic Galerkin and stochastic collocation finite volume methods for approximating random (fie...