A procedure for tidal analysis with a Bayesian information criterion

TLDR

The method assumes the drift in tidal data is smooth. The authors develop a computer algorithm for tidal analysis based on Ishiguro et al.'s Bayesian approach. The algorithm employs a Bayesian model with a smoothness prior, maximizes the posterior distribution, selects hyperparameters via Akaike's Bayesian Information Criterion, and is implemented in the BAYTAP‑G program to accommodate irregularities such as drift, steps, and meteorological disturbances. The program’s applicability is demonstrated on both simulated data and real strain measurements. Published in 1983.

Abstract

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.