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In this paper, the authors verified the formulated principles of the estimation of Gauss–Markov models in which estimated parameters X were random. For this purpose, methods for the prior definition of covariance matrix CX for the estimated parameters were provided, which were used to determine the conditional covariance matrix of observation vector L and then estimate the most probable values of parameters X̂. Covariance matrix Cov(X̂) obtained as a result of this estimation was used to define the limit values of the variance of these parameters. Practical application of the proposed method for the Gauss–Markov model estimation for random parameters was illustrated on a fragment of a leveling network of points to determine the vertical displacements of a landslide surface.

Application of Advanced Statistical Procedures for Adjustment of Results in Measurements of Displacements