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This analysis differs from the classical PSA in the manner of which perturbations are introduced on model parameters, specifically using impulses, and thus is named impulse parametric sensitivity analysis (iPSA).
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Some statistical distributions are introduced on the input model parameter (porosity) and the statistical distributions induced on the output parameters (cell voltage, resistance) are analyzed.
Therefore, most of the changes introduced in the model parameters have to be experimentally realizable.
Each of these models introduced one additional parameter.
This study introduces another model parameter to develop an enriched CMLM (ECMLM).
It has been suggested, though, that the increase in the phylogenetic signal and/or signal/noise-ratio due to concatenation has a much stronger effect on the resulting phylogenies than any bias introduced by averaging over model parameters.
Some models introduce one more parameter, p, which is used to obtain a more general relation.
Some models introduce one more parameter, c, which is used to obtain a general relation that includes the most common relations.
The density was introduced to descriptions by dependence of model parameters on density using the least square estimation.
Inspired by the observed linear relationship between evolutionary conservation and RSA, we here take the standard Goldman-Yang model of coding-sequence evolution (GY94, [ 21]) and introduce to it a dependency of the model parameters on RSA.
Concepts in learning model parameters are introduced, as well as model validation and testing.
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