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One more point about prior probabilities and Bayesian convergence should be mentioned here.
Convergence should be the first to be considered when we evaluate a control system.
Seeing how both images are relatively small to begin with, convergence should be relatively instant.
Careful balancing of divergence and convergence should be taken into account during the design stage of these projects.
Using these algorithms users can exchange their updates in any order since the convergence should be ensured in all cases.
Adaptive convergence should be common in behaviour, especially behaviour used for communication, because the environment often shapes the evolution of signal design.
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In this paper, we construct an analytical framework of the process of convergence that should be applicable to parareal simulations of general turbulent systems.
This computational remark suggests the possibility of using the iterative scheme also in case the condition L ≥ L 0 fails; however, its convergence properties should be theoretically analyzed.
The optimization procedure should satisfy the following principle: if the least-squares objective function is far from the minimum point, the convergence direction should be identical to that of the steepest descent algorithm; if the objective function is close to the minimum point, the convergence direction is the same as that of the Newton algorithm.
A rigorous examination of the validity of such a convergence comparison should be left to experts like Anthony King at the University of Essex, and Richard Pious at Columbia/Barnard.
When using Bayesian approaches, convergence diagnostics should be monitored and the influence of priors should be considered.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com