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Relative uncertainty is the measurement uncertainty relative to the magnitude of a particular single choice for the value for the measured quantity, when this choice is nonzero. This particular single choice is usually called the measured value, which may be optimal in some well-defined sense (e.g., a mean, median, or mode). Thus, the relative ...
Any non-linear differentiable function, (,), of two variables, and , can be expanded as + +. If we take the variance on both sides and use the formula [11] for the variance of a linear combination of variables (+) = + + (,), then we obtain | | + | | +, where is the standard deviation of the function , is the standard deviation of , is the standard deviation of and = is the ...
In this form the relative entropy generalizes (up to change in sign) both the discrete entropy, where the measure m is the counting measure, and the differential entropy, where the measure m is the Lebesgue measure. If the measure m is itself a probability distribution, the relative entropy is non-negative, and zero if p = m as measures.
For example, an experimental uncertainty analysis of an undergraduate physics lab experiment in which a pendulum can estimate the value of the local gravitational acceleration constant g.
The uncertainty on the output is described via uncertainty analysis (represented pdf on the output) and their relative importance is quantified via sensitivity analysis (represented by pie charts showing the proportion that each source of uncertainty contributes to the total uncertainty of the output).
This is "the amount of uncertainty remaining about after is known", and thus the right side of the second of these equalities can be read as "the amount of uncertainty in , minus the amount of uncertainty in which remains after is known", which is equivalent to "the amount of uncertainty in which is removed by knowing ". This corroborates the ...
It is a minimum uncertainty state, with the single free parameter chosen to make the relative ... this formula means that a coherent state remains unchanged by the ...
In imaging science, the RMSD is part of the peak signal-to-noise ratio, a measure used to assess how well a method to reconstruct an image performs relative to the original image. In computational neuroscience, the RMSD is used to assess how well a system learns a given model. [8]