A Bayesian approach to the detection of gross errors based on posterior probability

Existing methods for gross error detection, based on the mean shift model or the variance inflation model, have hardly considered or taken advantage of the potential prior information on the unknown parameters. This paper puts forward a Bayesian approach for gross error detection when prior information on the unknown parameters is available. Firstly, based on the basic principle of Bayesian statistical inference, the Bayesian method—posterior probability method—for the detection of gross errors is established. Secondly, considering either non-informative priors or normal-gamma priors on the unknown parameters, the computational formula of the posterior probability is given for both the mean shift model and the variance inflation model, respectively, under the condition of unequal weight and independent observations. Finally, as an example, a triangulation network is computed and analyzed, which shows that the method given here is feasible.

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