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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)}.
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If the pattern P i is generated from the C i, i.e., (4).
The signal f i is generated from the frequency standard of the satellite.
dk,p is generated uniformly from [1,15] m. κk,p=20 and ε k, p i is generated from a gaussian distribution N ( 0, ( σ k, p i ) 2 ).
Let us f i is a set of training images and g i is generated from the 2D Gaussian shape; then, H_{i}^ = frac{{G_{i} }}{{F_{i} }}, (2 where the division is performed element-wise.
For example, a feature c i is generated from a window of frames zi:i+h−1 by Eq. 10, where b∈R is a bias term and f is a non-linear function.
This product called L2-IBI is generated from magnetic field and plasma observations onboard Swarm, and gives information as to whether a Swarm magnetic field observation is affected by EPBs.
E i ⊲ E j : E j is informed E i. E i ∋ CP i : E i has a credential parameter CP i. E i ~ CP i : E i conveyed CP i. E i ≡ #(CP i ): E i persuaded that CP i is generated from proper entity.
First, Y i is generated from Bernoulli 0.5).
If j and k are unknown, u i is generated from.
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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