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Measured spectra can be fitted using a simple scheme for flagging model parameters as fit variables.
The SANS intensities can be fitted using a polydispese spherical particles model.
Moreover, the airflow distribution can be fitted using a unified dimensionless Gaussian function under different sparging pressures for a given porous medium.
Quasi-likelihood models can be fitted using a straightforward extension of the algorithms used to fit generalized linear models.
The data U,R could be fitted using a power law behavior U = aRb and the coefficient could be used as a classification tool of the road curves.
With a cross-validation procedure, we show that the hybrid gravity model can be fitted using a small portion of the data as training set, to correctly predict the remaining mobility flows.
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All of the models can easily be fitted using an Expectation Maximization (EM) algorithm.
The intensity histogram in a SPECT brain image can be fit using a positively skewed α-stable distribution.
Such a model can be fit using a maximum likelihood method [41].
Finally, zfDysCitrine recovery curves can be fit using a diffusion model.
Concentration response curves were fitted using a nonlinear interactive fitting program (Graph Pad Prism 4.0; GraphPad Software Inc).
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