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It has an advantage of no transformation since it is not shrunk easily even with falling temperature.
The reason is that even though partial branching decreases the cost of selecting valid neighbors, i.e., it narrows down the number of children of a branching node in each step, the size of search tree is not shrunk.
We, therefore, adopt an alternative hypothesis testing framework by conducting an effect size type analysis, in which the measure of statistical difference is the standard deviation (which is not shrunk by definition by the size of the sample).
If there is prior belief for a regulatory interaction, then corresponds to less shrinkage being incurred on the corresponding, hence making it more likely that this parameter is not shrunk out of the model.
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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