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For the latter class of method, rapid prediction of the mean up-crossing rates is essential.
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A class of methods having optimal stability characteristics is defined.
Energy issues have persisted for this class of methods.
The first class of methods relies on "suppositional" criteria to assign amino acids to modules, whereas the second class of methods relies on "coevolutionary" criteria for this task.
The second class of methods relies on Bayesian inference and Markov Chain Monte Carlo (MCMC) methods [3 5].
This class of methods depends on the finite difference method based on the Hermite formula.
In addition, this class of methods needs a large amount of adaptation data.
Parallel variations of the proposed class of methods are discussed below.
Therefore, the ABS class of methods coincides with those studied by Stewart [20].
This class of methods can process large data sets in reasonable time.
Another class of methods use matrix factorisation techniques to extract frames from a video sequence.
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