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Then, the method of least squares was applying to estimate system parameters.
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At this time, the estimator was applied to estimate the true change point.
The bootstrapping estimation method is applied to estimate the model parameters.
Maximum likelihood estimation (MLE) was applied to estimate the distribution parameters (Table 1).
An estimation of the PSR is applied to estimate the accuracy of this local dynamic map.
An estimation method that utilizes the sparsity constraint is applied to estimate the CAF.
The friction method was applied to estimate these costs.
DFRFT is applied to estimate the vibration acceleration.
Finally, FLRLSF is applied to estimate the target state.
Various techniques have been applied to estimate the C-factor.
Recurrent neural networks have been applied to estimate language models.
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