Therefore, we estimated the structure based on model parameter estimation examining which structures could reliably reproduce the experimental data with respect to the signal amplitude and duration of the signaling molecules.
On the anomaly detection side, a component library has been developed for Hidden Markov and Mixture Model parameter estimation and classification.
Here, we describe Monte Carlo methods developed to include highly-correlated instrument performance uncertainties in typical astrophysical model parameter estimation studies.
The identification procedure includes optimal experiment design, regression vector selection and model parameter estimation.
Next, we review of some relevant techniques for model parameter estimation.
However, the existing research focuses either on thermal or electrochemical model parameter estimation.
Domain expertise is adopted in model parameter estimation to facilitate the computation.
Implementation issues for modal identification and structural-related model parameter estimation are addressed.
This allows us to quantify model parameter estimation uncertainties as well.
The time-domain analysis of tracer response data was used for the flow model parameter estimation.
If spatially highly resolved precipitation was used during model parameter estimation, the predictive uncertainty of the model was clearly reduced.
