A Bayesian Hierarchical Space-Time Model for Significant Wave Height

  • Erik Vanem
Part of the Ocean Engineering & Oceanography book series (OEO, volume 2)


This chapter presents a Bayesian hierarchical space-time model for significant wave height. This type of models was selected based on a comprehensive literature survey and the framework allows modeling of complex dependence structures in space and time. Such models may incorporate physical features and prior knowledge, yet remain intuitive and easily interpreted. The model presented in this chapter has been fitted to significant wave height data with different temporal resolutions for an area in the North Atlantic Ocean. The various components of the model will be outlined, and the results from applying the model on monthly and daily data, as well as monthly maximum data, will be discussed. A few different model alternatives have been investigated and long-term trends in the data have been identified with all model alternatives. Overall, the identified trends are in reasonable agreement and also agree fairly well with previous studies.


Credible Interval Significant Wave Height Noise Term Marginal Posterior Distribution Integrate Nest Laplace Approximation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Mathematics DepartmentUniversity of OsloOsloNorway

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