Bayesian Hierarchical Space-Time Models with Application to Significant Wave Height

• Main Author: Vanem, Erik. (Author)
• Corporate Author: SpringerLink (Online service)
• Format: Electronic
• Language: English
• Published: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2013.
• Series: Ocean Engineering & Oceanography, 2
• Subjects: Statistics.; Distribution (Probability theory).; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.; Probability Theory and Stochastic Processes.; Geophysics and Environmental Physics.
• Online Access: https://ezaccess.library.uitm.edu.my/login?url=http://dx.doi.org/10.1007/978-3-642-30253-4

This book provides an example of a thorough statistical treatment in space and time of ocean wave data. It is demonstrated how the flexible framework of Bayesian hierarchical space-time models can be applied to oceanographic processes such as significant wave height in order to describe dependence s...

Table of Contents:

• Preface
• Acronyms
• 1.Introduction and Background
• 2.Literature Survey on StochasticWave Models
• 3.A Bayesian Hierarchical Space-Time Model for Significant Wave Height
• 4.Including a Log-Transform of the Data
• 6.Bayesian Hierarchical Modelling of the Ocean Windiness
• 7.Application: Impacts on Ship Structural Loads
• 8.Case study: Modelling the Effect of Climate Change on the Worlds Oceans
• 9.Summary and Conclusions
• A.Markov Chain Monte Carlo Methods
• B.Extreme Value Modelling
• C.Markov Random Fields
• D.Derivation of the Full Conditionals of the Bayesian Hierarchical Space-Time Model for Significant Wave Height
• E.Sampling from a Multi-normal Distribution.