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In general, an ensemble of Tanner graphs is defined through degree distribution pairs.
Convergence bounds are calculated only for limited Bayesian mixture class/prior distribution pairs (for example, uniform prior/Epanchinkov kernel) [107].
In this algorithm, we calculate the probabilities for all possible isotopic distribution pairs.
This article has presented classification and regression models for assigning scores to potential, matched isotopic distribution pairs in mass spectra.
In order to compare the resulting flux distributions for the individual time points, normalized Hamming distances were computed for all flux distribution pairs (see Additional file 10).
Once we have an algorithm that annotates the matched isotopic distribution pairs accurately, we will then be able to apply it in a number of contexts to aid in peptide identification and quantification.
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Therefore, for a given degree distribution pair, we have the following equalities: g F ≤ g M ≤ g L, (32).
This experiment is repeated 100 times for every CSFR and distribution pairing to ensure accuracy, especially for small CSFR values.
The optimal power distribution pair (left (P^_{n},P^_{m}right)) can be obtained by Eq. (19).
In [16], Richardson et al. state that for short LDPC codes, it is not always best to pick the degree distribution pair with the best threshold.
Bond angle distribution, pair correlation functions and shortest path ring analysis were used to determine the nature of plastic deformation due to the sliding.
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