Bayesian inference for long-term prediction of significant wave height
This paper considers the problem of estimating long-term predictions of significant wave-height. A method which combines Bayesian methodology and extreme value techniques is adopted. Inferences are based on the Metropolis–Hastings algorithm implemented in an appropriate Markov Chain Monte Carlo sche...
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| Veröffentlicht in: | Coastal engineering (Amsterdam) 2007-05, Vol.54 (5), p.393-400 |
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| container_title | Coastal engineering (Amsterdam) |
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| creator | Scotto, M.G. Guedes Soares, C. |
| description | This paper considers the problem of estimating long-term predictions of significant wave-height. A method which combines Bayesian methodology and extreme value techniques is adopted. Inferences are based on the Metropolis–Hastings algorithm implemented in an appropriate Markov Chain Monte Carlo scheme. The method is applied to obtain return values of extreme values of significant wave height collected on the northern North Sea. The results are compared with those obtained by Guedes Soares and Scotto [Guedes Soares, C. and Scotto, M.G., 2004. Application of the r-order statistics for long-term predictions of significant wave heights.
Coastal Engineering, 51, 387–394]. |
| doi_str_mv | 10.1016/j.coastaleng.2006.11.003 |
| format | Article |
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| ispartof | Coastal engineering (Amsterdam), 2007-05, Vol.54 (5), p.393-400 |
| issn | 0378-3839 1872-7379 |
| language | eng |
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| source | Elsevier ScienceDirect Journals |
| subjects | Bayesian inference Earth sciences Earth, ocean, space Engineering and environment geology. Geothermics Engineering geology Exact sciences and technology Extreme value models Geomorphology, landform evolution Marine Marine and continental quaternary Markov Chain Monte Carlo Return value Sea-levels Surficial geology |
| title | Bayesian inference for long-term prediction of significant wave height |
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