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The tensor polynomial form of the maximum stress criterion is used in conjunction with a progressive failure simulation methodology.
Here, we first express the form of the Maximum A-Posteriori (MAP) estimate for Laplace priors and then use the Monte-Carlo-based Randomize Maximum Likelihood (RML) method to generate approximate samples from the posterior distribution.
Accordingly, we can express the analytic form of the maximum discount factor in a DRG when assuming that channel gains |g i |2 lie in a compact set (left [nu _{i}^{min }, nu _{i}^{max }right ]) [18].
Since we fix the form of the maximum likelihood estimator Eq. (24), this two-step estimation, instead of simultaneous estimation, does not bias the result, especially the dependence structure between UV and IR, unless the assumed univariate LF shape would be significantly different from the real one.
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Also the goal of maximizing strength of a structure should, to the authors mind, be in the form of minimizing the maximum stress under all load cases.
The closed form solution of the maximum absorption power and corresponding power takeoff parameters are obtained.
The models provide the closed form solutions of the maximum likelihood estimating equations for the parameter estimation under a Bernoulli setup.
A modified form of the Discrete Maximum Principle (DMP) is derived for systems with finite memory.
Then we provide a comparison result, based on a form of the weak maximum principle at infinity, which together with the "a priori" estimates previously obtained, yields uniqueness under very general Ricci assumptions.
Indeed, if w is a solution of (18) for some μ ≤ 0, then using again condition (15), together with the interior form of the parabolic maximum principle and the Hopf boundary point lemma (see, e.g., [[14], Chapter III.13]), we conclude that w = 0. □.
Closed-form solutions of the maximum deflection and the effective elastic modulus in both kinds of nanowires are achieved.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com