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The large CI in our sample represented uncertainty due to the small number of patients in each group.
We scored responses such that 15 20 points represented a score indicating clear recognition of scientifically inappropriate teleological reasoning, 10 14 points represented uncertainty about the appropriateness of teleological expressions, and a score less than 10 represented an inability to recognize teleological expression as biologically incorrect.
We did not estimate separate model parameters for process variance or observation error because we represented uncertainty using binomial distributions.
We represented uncertainty by 95% central prediction intervals.
We represented uncertainty due to sampling variation in both the unadjusted and adjusted cost-effectiveness ratios using non-parametric bootstrapping.
Over the past decades, the number of scenarios has increased from one to multiple scenarios, thereby increasing the represented uncertainty range.
Similar(53)
Fuzzy logic distinguishes different ways to represent uncertainty.
Implications for representing uncertainty in IA models are discussed.
There now exists a wide variety of formal devices for representing uncertainty (Halpern 2003).
The approach considers two spatial scales for representing uncertainty: local and global.
Fuzzy logic can be used both to represent uncertainty and imprecision [23].
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CEO of Professional Science Editing for Scientists @ prosciediting.com