The regression coefficients are usually estimated using maximum likelihood estimation. Other approaches employ clustering based on maximum likelihood estimation. Some estimators are highly sensitive to outliers, notably estimation of covariance matrices. The above solutions minimize the variance of the output estimation error. This is most often done using methods of statistical estimation. Kinetic parameters are frequently determined from experimental data via nonlinear estimation. A framework for improved use and interpretation of estimation error measurement is included in. The following is a simple example of parametric empirical Bayes estimation. This is a particular way of implementing maximum likelihood estimation for this problem. It is a popular algorithm for parameter estimation in machine learning. It is one of a number of approaches to spectral density estimation. This parametrization may be useful in Bayesian parameter estimation. This is a restatement of the maximum a posteriori estimation method. One method of estimation is ordinary least squares. It is a measure of the discrepancy between the data and an estimation model.