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The main challenge of this type of modeling lays in the estimation of the model parameters which may not be directly measurable from clinical investigations.
Next section deals with the estimation of the model that best fits these samples.
Finally, our work here focuses on estimation of the model parameters.
The results from the estimation of the model are presented in Table 8.
In the estimation of the model, several explanatory variables are tested according to a stepwise procedure.
Estimation of the model parameters may then be carried out using the likelihood method.
Algorithm A: Simultaneous estimation of the model parameters and the background intensity A1.
We discuss the estimation of the model parameters by maximum likelihood.
Two sets of experimental data were used for the estimation of the model parameters.
Estimation of the model parameters by maximum likelihood is investigated in Section 6.
The estimation of the model parameters is carried out by the method of maximum likelihood.
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test of the model
valuation of the model
figure of the model
assessment of the model
estimating of the model
appreciation of the model
prediction of the model
estimation of the models
estimates of the model
assessments of the model
estimation of the regression
estimation of the matrix
estimation of the magnitude
estimation of the signal
estimation of the saturation
estimation of the detector
estimation of the post
estimation of the complexity
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