By Erik Vanem (auth.)
This booklet offers an instance of a radical statistical remedy of ocean wave info in house and time. It demonstrates how the versatile framework of Bayesian hierarchical space-time versions could be utilized to oceanographic techniques similar to major wave peak for you to describe dependence constructions and uncertainties within the data.
This monograph is a learn publication and it really is partially cross-disciplinary. The technique itself is firmly rooted within the statistical examine culture, in keeping with chance concept and stochastic strategies. even though, that technique has been utilized to an issue within the box of actual oceanography, interpreting info for major wave top, that is of the most important value to ocean engineering disciplines. certainly, the statistical homes of important wave peak are vital for the layout, development and operation of ships and different marine and coastal constructions. additionally, the ebook addresses the query of no matter if weather switch has an impact of the sea wave weather, and if that is so what that influence will be. hence, this booklet is a crucial contribution to the continuing debate on weather switch, its implications and the way to conform to a altering weather, with a selected concentrate on the maritime industries and the marine setting.
This ebook might be of price to a person with an curiosity within the statistical modelling of environmental strategies, and specifically to these with an curiosity within the ocean wave weather. it truly is written on a degree that are supposed to be comprehensible to each person with a uncomplicated historical past in facts or effortless arithmetic, and an advent to a few uncomplicated ideas is equipped within the appendices for the uninitiated reader. The meant readership contains scholars and pros considering information, oceanography, ocean engineering, environmental learn, weather sciences and threat review. additionally, the book’s findings are correct for varied stakeholders within the maritime industries reminiscent of layout places of work, category societies, send vendors, yards and operators, flag states and intergovernmental businesses comparable to the IMO.
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Additional resources for Bayesian Hierarchical Space-Time Models with Application to Significant Wave Height
To obtain as good predictions as possible for locations where wave data are available. In the following, some of these will be briefly reviewed, even though it is noted that the aim of this study is to extend the scope and broaden the perspective of the statistical models to also include the spatial dimension. A method for calculating return periods of various levels from long-term nonstationary time series data of significant wave height based on a new definition of the return period is presented in [182, 183].
Bayesian statistics is a branch of statistics relying on Bayes’ theorem, named after Thomas Bayes (1701–1761) who first used it. Pierre-Simon Laplace later stated the theorem in a more general form in 1812. Basically, it expresses how a subjective degree of belief should rationally change in light of evidence or data. Hence, it involves a prior belief, evidence or data, and a posterior belief or posterior probability that has been updated based on the evidence. 2 Stochastic Modeling of Environmental Processes 15 of belief of event A in light of the evidence X , often referred to as the posterior probability of A.
The times involved in such models are normally in the order from a few seconds to a couple of hours. The long-term models mainly refer to the description of spectral parameters, and the times that are involved normally span over many years. It is the latter time scales that are of main interest in the present work, considering modeling of possible long-term trends due to climate change. However, there are tools, to be discussed later, for combining the long-term statistics of significant wave height with short-term statistics of individual wave heights in order to estimate for example extreme crest heights.
Bayesian Hierarchical Space-Time Models with Application to Significant Wave Height by Erik Vanem (auth.)