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To adopt the models is simply to suppose that observable events and states of affairs are as if the models were true, but there is no need to commit oneself to the existence of the unobservable entities and processes that figure in the models.
In order to answer the firs question, usually the goodness-of-fit is determined, i.e. the distance between model and data is related to the expected value if the models were true: a χ-test is applied (Press et al., 1999).
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None of the models are true continuous random networks.
These results were generalized to the important case of nonnested models by Cox in [ 16, 17] and to the case of neither of the competing models being true by Vuong [ 15].
If this model were true, we would detect an intermediate phenotype in strong CSN5 and Nedd8 mutants.
If our model were true, then the canonical PAK-1 activity would be predicted to be GTPase-dependent.
If such a model were true, we would expect that miRNAs predominantly target genes encoding transcription factors.
If the Instantaneous Model were true, cells should pass Start at or near the peak of Cln3 profile during G1.
What grounds do biologists have for believing that what is true of a mere model is true of many different organisms?
Still, this latter falsehood seems quite a bit closer to the truth than its predecessor (assuming, again, that the standard model is true).
If the rival models are not nested then, depending on which model is true, two different Kullback Leibler distances may be defined.
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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