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In general, testing a model needs more sample sizes and stress levels than estimating model parameters.
This leads to a fundamental difficulty in estimating model parameter values.
The proposed methodology for estimating model parameters can be extended to many other computer vision algorithms.
Personalized tumor growth modeling allows patient-specific prediction by estimating model parameters based on individual's images.
The last case study illustrates the optimal design of experiments for estimating model parameters with maximal accuracy.
Usually traditional regression methods for estimating model parameters fail in this setting, since they overfit the data.
However, the 6 DOF model makes challenges in estimating model coefficients and designing the model-based control.
Thus, we go for estimating model stability to ensuring acceptability of both long run and short run coefficient of the study.
Additionally, separating the individual and collective dynamics supports efficient and flexible model building and simplifies the process of estimating model components from empirical data [39].
Stone [2] suggested the use of leave-one-out cross-validation for estimating model parameters and for assessing their predictive error.
These (mixture) item response models have proven to be beneficial for estimating model parameters accurately in the context of rapid guessing behavior.
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