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The deviation of anchoring joint is transformed into the length error of radial cable, and the member length error is assumed to yield Gaussian distribution.
In this paper we study the problem of estimating the density of the error distribution in a random design regression model, where the error is assumed to be independent of the design variable.
Most often, a zero-mean uncorrelated Gaussian prediction error is assumed, although in many engineering applications prediction errors will show non-negligible spatial and/or temporal correlation (e.g. when densely populated sensor grids are used).
If this relative error is assumed for the whole continent, water balance calculation using the three products over the whole Australian continent results in 144.7 ± 11.3 mm/year estimated total runoff to the surrounding oceans during the study period.
The error is assumed to be stochastic; see Section 4.5.
Then, the gaze error is assumed as 1 button.
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A classic error is assuming that everything a team does has to be collaborative.
This contrasted with earlier models wherein the random error was assumed to be constant over time.
Bounds on the model structure error are assumed unknown, or known but conservative.
Measurement error was assumed to be Gaussian distributed with zero mean.
However, the measurement error was assumed to be of a fixed value to simplify the calculation.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com