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A computer algorithm for tidal analysis is developed, based on a Bayesian method proposed by Ishiguro et al. (1983). The basic assumption of the method is smoothness of the drift. This assumption is represented in the form of prior probability in the Bayesian model. Once the prior distribution is determined, the parameters used in the analysis model are obtained by maximizing the posterior distribution of the parameters. For the given data, ABIC (Akaike's Bayesian Information Criterion, Akaike 1980) is used to select the optimum values of the hyperparameters of the prior distribution and combination of parameters. The program, BAYTAP-G, can be adapted to tidal data which includes such irregularities as drift, occasional steps and disturbances caused by meteorological influences. The applicability of this program is examined using simulated data and real strain data.
Tamura et al. (Mon,) studied this question.