One way to learn more about the stochastic model is variance component estimation. In this case a scale factor for the variances of each observation type is determined. For example, a separate scale factor is determined for the variances of all distances, angles, or GPS vectors. Variance component estimation thus determines the relative weighting of groups of heterogeneous observations. Identifying the stochastic model is not always unambiguously possible.
Typical examples are multipath and the ionosphere, both a function of time.
The receivers measure carrier phases to approximately one-hundredth of a cycle. This measurement accuracy should be reflected in the stochastic model. Blunders in observations will, naturally, increase the a-posteriori variance of unit weight. While large blunders are easy to identify and eliminate, there is a problem with small blunders. Unfortunately, least-squares adjustments have at least one undesirable property in that if one blunder exists in the data, several observations usually receive large residuals.