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vector v N, where samples v n are driven from distributions PY∣X and PZ∣X, respectively, which gives the following estimates: α ̂ = 1 N trials ∑ N trials i = 1 ϕ ( ( v N ) ( i ) ; ℋ 1 ), ( v n ) ( i ) being generated from P Y ∣ X β ̂ = 1 N trials ∑ N trials i = 1 ϕ ( ( v N ) ( i ) ; ℋ 0 ), ( v n ) ( i ) being generated from P Z ∣ X.
The log-likelihood, L i,j), of sequence O i being generated from model λ j reflects the degree to which O i fits λ j and is defined as: textbf{L}(i,j =log{Prleft(O_{i}|lambda_{j}right)}.
Figure 2b shows the second character 'I' being generated in the same way.
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κ k i is generated uniformly from [20,25].
Using the observations left, matrix R ~ ( i ) is generated.
If the pattern P i is generated from the C i, i.e., (4).
σ k, p i is generated uniformly from the interval [1,8] for each i and k.
Hence, spatial entropy E i for cluster μ i is generated.
First, the tree of all paths originating from an atom a i is generated.
In other words, it measures the time since bundle i was generated by its source node.
The signal f i is generated from the frequency standard of the satellite.
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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