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This distribution allows for quantification of uncertainty and bias.
As explained in van den Berg (2001), this distribution allows the model to verify the mixed proportional hazard assumption.
Interestingly, this distribution allows a Gaussian mean-scale mixture (GMSM) representation, which implies that the distribution can be reformulated as hierarchically (conditionally) Gaussian [9, 10].
Although perfect channel knowledge (PCK) is assumed for the derivation of the biased distribution, this distribution allows accurate BER estimation in a wide range of scenarios, such as pilot-based [7] or blind channel estimation, and in the presence of residual frequency offset, IQ imbalance, and phase noise.
This distribution allows consumers to exercise some choice of facility and/or provider.
This distribution allows SNAREs to increase their force output as CTD zippering draws two membranes into close proximity.
Similar(52)
The features of this distribution allow one to predict the mode shapes and give an indication of the additional masses number and weight.
This posterior distribution allows us to maintain probabilistic estimates without committing to a defined gaze, and this is advantageous for further reasoning about overall scene behaviour.
This bimodal distribution allows us to derive a cutoff to separate cases into high and low expression groups by fitting two normal distributions to the data.
This irregular uniform distribution allows to allocate the extra-repair symbols with (bar {f} =frac {E}{2}) and (f_{e}=frac {1}{E+1}) for (e in {0,1,dots,E}).
Using the negative binomial distribution allows us to estimate this parameter separately, yielding 11.4 % instead.
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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