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where the prediction error (residual), e,is given by (11).
The central equation in a posteriori theory is the error residual relationship.
We introduce two basic ingredients of our error bounds: the error residual relationship and coercivity lower bounds.
Because of the complex nature of the granulation process, the error residual is exploited further in order to improve the model performance using a Gaussian mixture model (GMM).
where the prediction error (residual) signal is given by eleft i,mathbf{W}right)=d(i -{mathbf{X}}^T(i -{mathbf{X}2).
This allows us to write the error residual equation (39) as a ( e , v ; μ ) = ( e ˆ , v ) X, ∀ v ∈ X N, (42).
Similar(47)
The RRU reduces the bitrate by coding the prediction error residuals at a reduced spatial resolution.
This model also outperformed the polynomial model remarkably in terms of predicting error, residuals range and the correlation coefficient between the experimental and predicted MRR values.
This implies that the error residuals (after maximum likelihood estimation) may be used equivalently instead of (24) for associating the multipath with the walls.
In order to verify the camera calibration, error residuals were computed using a bundle and triangulation approach.
We allowed covariances (correlations) among the predictor variables and significant covariances among the error residuals of the outcome variables.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com