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Instead of randomly assuming the relationships between the target classes in the MLC setup like in CC, BN properly defines all relevant conditional dependencies.
For each read, an amplicon instance was selected randomly (assuming evenl representation of all amplicons in the pool), and a step number was randomly selected for that amplicon with the probabilities of various steps weighted as specified.
Multilocus genotypes in offspring were generated randomly, assuming simple Mendelian inheritance from the two randomly chosen parents.
We model both males and females, with sex being determined randomly assuming an even sex ratio at birth and in the initial population.
Ancestral sequences are generated randomly assuming that the base frequency of all 61 non-stop codons are equal, and all 27 one-step mutations at a codon are allowed.
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One of my faulty assumptions — I randomly assumed he's going to know.
As described previously, they can randomly assume the sub-share of P4: (m^_{4}=0) and generate an interpolation polynomial (phantom {dot {i}!}f^(x)=6+2x+2x^{2}+3x^{3}) correspondingly.
First, we randomly assumed that 1 5% of our population might be African.
For each simulation run, N data and interfering symbols are randomly generated assuming shifted 16-QAM modulation constellation is used (M=16).
For each pathogen or group of pathogens, P i, ComSS i, and OutSS i were randomly drawn assuming a β distribution for each, and the 2 incidence equations were recalculated.
The model of random encounters is almost certainly imperfect because individuals will not necessarily be encountered randomly as assumed by this 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