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One of the main objectives is to show how to determine the structure and parameters of the neural network as well as how to estimate the modelling uncertainty of the resulting neural model.
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The method of pseudolikelihood estimation was used to estimate the models.
Confirmatory factor analyses with full information maximum likelihood estimation (FIML) was used to estimate the models.
After that, we discuss how to estimate the model parameters.
We use Maximum Likelihood (ML) to estimate the model.
The Bayesian method is employed to estimate the model parameters.
A Bayesian approach is used to estimate the model parameters.
To estimate the model parameters in the above mathematical expression, the hierarchical Bayesian model is used to estimate the model parameters ( {sigma}_{co}^2 ), ( {sigma}_{bl}^2 ), and ( {sigma}_n^2 ).
SUV was used for semi-quantitative analysis and model fitting to estimate the model parameter values.
A random parameter logit model was implemented to estimate the model.
The algorithm used to estimate the model parameters converged for all current models.
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