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Sentence examples for model parameters rather from inspiring English sources

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In Bayesian approach, interest lies in estimating posterior distributions of model parameters rather than individual parameter values and asymptotic standard errors.

The DLR data were not used to find model parameters; rather it was used to have an assumption of the validity of the models.

The benefit of a Gaussian random walk model is that it has very few model parameters rather than a parameterized process model as mentioned in Section 2.3.1.

As the DLR data and NASA SSE data are actually estimated data, they were not used to find model parameters, rather they are just used to show a comparison among the results.

An advantage of Bayesian inference is that one obtains a posterior probability over the model parameters rather than just a point estimate.

Similar(54)

The goodness of fit in each case demonstrated that explicitly selecting leaf temperature as a model parameter, rather than including temperature intrinsically as is usually done in more complex models, was warranted.

The public health practitioner may feel that robustness to 10% uncertainty in the model parameters is rather small, given the substantial uncertainty in the epidemiological dynamics of TB with HIV prevalence.

This principle states that whenever one seeks to control a system, first by getting single-point estimates of the model parameter values (rather than estimating the entire parameter distributions) and then using these single point estimates to control the system, the control is done suboptimally.

As a consequence, detailed insight into the processes at the microscale is lost, and the coarse scale model parameters are apparent rather than physical parameters.

This method provides a solution to the problem of analyzing gene regulatory networks under parameter uncertainties because the root locus method considers the model parameters as variable, rather than fixed.

Although only 3 islands were surveyed on all 4 years (Table S3), the unbalanced nature of the data was not an issue for our approach because we estimated model parameters via maximum likelihood rather than least squared methods, the former not requiring a balanced design in time.

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