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Thus, this application provides an example when the superiority of the π r 1 and π r 2 priors matters to the conclusion that in this data set the errors distribution is leptokurtic.
When using the minimal training set, the errors are larger but the performance is still quite good, with errors only slightly larger than the axial resolution of the system.
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Set the error verbosity level to 3 to print detailed information, then retrieve the error buffer.
Set the error verbosity level to print detailed information when errors occur.
Set the error verbosity level to 3 to print detailed information.
Set the error verbosity level to 0, so that errors are returned as numeric error code values rather than as strings.
Populate the "s" structure with attribute values, then use it to set the error style to capped and the error color to red for the curve with the id "crv1".
We set the error tolerance for all algorithms as ε=10−4.
Consequently, it is reasonable to set the error threshold ξ depending on noise power.
We add 1% random noise in the synthetic data and set the error floor as 1% in the inversion.
Each grey line represents a point in the confidence set; the error bars are superimposed for convenience.
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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