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Unfortunately, there exists the problem that the classical maximum likelihood estimator for these models is sufficiently sensitive to outliers.
It is believed that the mathematical model developed, avoiding the drawbacks of spectral lines and bands models, is sufficiently accurate and convenient for engineering calculations as well as for incorporation in computational fluid dynamics codes.
However, the discrepancy in strain results produced by these models is sufficiently small that the benefit gained in terms of ease of modeling makes the thermally graded approach preferable with respect to many research questions.
The selected parameters ensure that the quality of the produced models is sufficiently high to be able to reliably measure properties described below as demonstrated by the last CASP experiment (http://predictioncenter.org/CASP9).org/CASP9
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While quantitative methods to account for uncertainty must aid decisions if the underlying models are sufficient approximations of reality, whether such models are sufficiently accurate has not yet been examined.
Although further study is needed to reduce some core uncertainties within the analysis, the model framework is sufficiently flexible to allow for the integration of new scientific information.
Therefore, we consider that the results obtained with graph models are sufficiently close to the standardized model parameters.
It is recognized that existing quality models are sufficiently accurate to estimate listening perceived quality of speech sequences subject to independent packet losses using PLR metric.
These differences are significant only if the processes' mathematical models are sufficiently accurate for establishing appropriate trade-offs between investment and cash flow.
It is shown that the performance of ASOG and UNIFAC models are sufficiently valid in representation of data base and in extraction calculations.
The following results show that the two models can be distinguished for parameter values such that the two models are sufficiently different, which will be defined below in Sect.
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