The method discussed renders the uncertainty evaluation more tangible to modellers and receivers of energy model scenarios.
First, our measured TUCF magnitudes are small, which render large uncertainties in the separation and evaluations of δ Grms from the background instrumental noise (experimentally, our TUCF signals become hardly distinguished from the instrumental noise as T ≳ 8K).
Yet there is a growing recognition that ignoring model and parameter uncertainties render model predictions prone to statistical bias and underestimation of uncertainty.
This is of course the best way to conduct a controlled experiment, where any uncertainty renders the dataset less useful.
A design model with neglected dynamics and parametric uncertainty renders invalid the separation theorem of and requires a revised pole placement.
Design codes typically recommend generic safety factors on the design life, but the heightened uncertainty renders the use of such safety factors questionable.
Although classical first-order logic is the de facto standard logical foundation for artificial intelligence, the lack of a built-in, semantically grounded capability for reasoning under uncertainty renders it inadequate for many important classes of problems.
Further, the policy implication is that the central bank's monetary policy under model uncertainty renders, in terms of the sacrifice ratio, the output-cost of inflation stabilization more important.
For extremely small sample sizes (for example, less than five years of yield records), this approach is simply not applicable because the data series length falls outside of the bounds of applicability, and as commented earlier, would render very large uncertainties in the statistical inference.
