What Is Model Covariance?
Uncertainty in the model is called model covariance. If you estimate model uncertainty data, this information is stored in the Model.CovarianceMatrix model property. The covariance matrix is used to compute all uncertainties in model output, Bode plots, residual plots, and pole-zero plots. Computing the covariance matrix is based on the assumption that the model structure gives the correct description of the system dynamics. For models that include a disturbance model H, a correct uncertainty estimate assumes that the model produces white residuals. To determine whether you can trust the estimated model uncertainty values, perform residual analysis tests on your model, as described in Residual Analysis. If your model passes residual analysis tests, there is a good chance that the true system lies within the confidence interval and any parameter uncertainties results from random disturbances in the output. In the case of output-error models, where the noise model H is fixed to 1, comput
