Titlebook: Bayesian Hierarchical Space-Time Models with Application to Significant Wave Height; Erik Vanem Book 2013 Springer-Verlag Berlin Heidelber

–DOX

明確

https://doi.org/10.1007/978-3-642-30253-462P12, 60K40, 86A32, 60H30, 60G15; Bayesian hierarchical modelling; Climate change; Ocean waves; Probabi

不理會

978-3-662-52197-7Springer-Verlag Berlin Heidelberg 2013

流眼淚

Bayesian Hierarchical Space-Time Models with Application to Significant Wave Height978-3-642-30253-4Series ISSN 2194-6396 Series E-ISSN 2194-640X

epidermis

輕浮思想

Including a Log-Transform of the Data,here are two main motivations for this; performing a log-transform of the data yields a model with greater estimated trends for rougher sea states compared to more moderate conditions and the log-transform could account for observed heteroscedasticity in the data. This chapter is based on material previously presented in [., .].

最后一個

FOVEA

Bayesian Hierarchical Modeling of the Ocean Windiness, significant wave height is not due to an increase in locally generated wind waves, but rather to increased swell. This observation is also consistent with studies that have suggested a poleward shift of the main storm tracks.

Hirsutism

insert

Book 2013tal structures. Furthermore, the book addresses the question of whether climate change has an effect of the ocean wave climate, and if so what that effect might be. Thus, this book is an important contribution to the ongoing debate on climate change, its implications and how to adapt to a changing c