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So, for each hypothesis hj (including hi), ∑en∈En P[en | hj·b·cn] = 1.
From the residual estimate, we calculate a correlation noise estimate for each hypothesis.
For each hypothesis, a prediction is made for each object state in the next frame.
If the error for each hypothesis is given as r h = f - f ̂ h, (13).
For the multi-hypothesis system, a residual frame estimate is required for each hypothesis.
The search process (choice of the interest zone, detection, and update) is recursively re-iterated for each hypothesis with different combinations of the interest zones.
Based on the observed characteristics of N-best hypotheses, the normalizing factors of NNLM for each hypothesis are approximated as a global constant for fast evaluation.
For each hypothesis test, corresponding amount of offset is removed from the (tilde {y}_{f}(n)) and correlated with the PSS signal of obtained SID.
Monte Carlo simulation, with the number of permutations set to 4,499, is used to calculate the permutation p value for each hypothesis test.
Based on the statistic observations, we propose to approximate the normalizing factors for each hypothesis as a constant proportional to the number of words in the hypothesis.
The conditional estimate of the object states was evaluated and combined with the individual estimate for each hypothesis, weighted by the corresponding probability function.
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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