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Quite recently, (L^{2}) (mean square) strong averaging principle for multivalued stochastic differential equations with Brownian motion was established by Xu and Liu [14].
Indeed Li [ 37] has shown that the Poisson τ-leap method has mean square strong order 1 2 and weak order 1 and this is consistent with the previous remarks.
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Furthermore, (mathbb{L}^{2} -strong convergence (also called mathbb{L}^{2} -strongnvergenconvergencealsog princalled for several types of slow-fast stochastic dynamical systems driven by Brownian noise has been investigated by Freidlin [5], Golec [15], Wang [16], and Fu et al. [17, 18].
In [8], Mao considered exponential stability in the mean square sense for the strong solutions of linear stochastic differential equations.
By using the tools of stochastic analysis and algebraic graph theory, a sufficient condition is obtained for the protocol to ensure strong mean square consensus under the fixed topologies.
The mean square displacements values show a strong departure from the predictions of the fluctuation-dissipation theorem.
The confirmatory factor analysis showed a strong fit with a root mean square error of approximation of 0.069 and a comparative fit index of 1.00.
The second metric is the root mean square error (RMSE, Eq. (9)), which is stronger influenced by large errors than by small ones.
In summary, PCA makes a very strong case for the use of root mean square on different channels and frequency bands.
Very strong models were achieved for kaolinite, illite and smectite and the root mean square error of cross validation (RMSECV) were all < 12 wt.%.
In each case, multiple strong candidate relationships that recognized the first four peaks were identified using r 2 and root mean square error (RMSE) values.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com