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The measurement errors consist of two parts: systematic error and random error. For many techniques, the
systematic errors have been greatly eliminated or supressed due to careful calibration, investigation and
modeling. Therefore the measurement error is considered to be primarily random error and to obey Gaussian
distribution. In most cases of data analysis, we have a good apriori model for the parameters hence only
the adjustments of the apriori model are estimated. Since the adjustments are usually several orders smaller
than the parameters, we can use the linear algebra to estimate the adjustments. For the errors obeying Gaussian
distribution, the optimal solution is the least squares estimation.
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