Sentence examples for model parameters initial from inspiring English sources

Living organisms are natural complex systems and modeling may play a crucial role since models can also be built with approximate and imperfect knowledge of the phenomenon, and model parameters (initial data, entities, relations between entities) can be adjusted to fit modeling results to experimental measurements [ 1].

Global sensitivity analysis is first used to identify sensitive model parameters (initial values estimated from batch cultures).

However, algorithms based on Kalman filter require complete knowledge of model parameters (initial states, state transition matrix, and noise covariance matrices) for implementation.

Since the quality of designs for GLMs depends on the model parameters, initial parameter estimates need to be provided by the experimenter.

Assessment of uncertainty in land surface models is complex, mainly because of the numerous sources of error from model parameters, initial states, input forcing data, and the model structure.

Our statistical formulation also explicitly considers the uncertainty in model inputs (model parameters, initial conditions), the analytical/sampling error associated with the field data, and the discrepancy between model structure and the natural system dynamics (e.g., missing key ecological processes, erroneous formulations, misspecified forcing functions).

The evolution of the drop radius, the flow and solute concentration profiles near the triple junction, the contact line mobility relationship, the apparent contact angles and the shape of the dissolution boundary during spreading are investigated as functions of initial Sn concentration, model parameters and initial conditions.

Pluto's climate and ice cycles are found to be very sensitive to model parameters and initial states.

A process unit model consists of several FORmula TRANslation (FORTRAN) subroutines collected in the model library and a data file for model parameters and initial values.

Threshold policies are also seen to be robust to uncertainty of model parameters, and initial conditions, as well as to delays between stock assessment and policy enactment.

Simulation of the extended system enables prediction of the propagation of probabilistic uncertainties associated with the model parameters and initial conditions, and to obtain the output probability densities.