Coverart for item
The Resource Bayesian hierarchical space-time models with application to significant wave height, Erik Vanem, (electronic book)

Bayesian hierarchical space-time models with application to significant wave height, Erik Vanem, (electronic book)

Label
Bayesian hierarchical space-time models with application to significant wave height
Title
Bayesian hierarchical space-time models with application to significant wave height
Statement of responsibility
Erik Vanem
Creator
Author
Subject
Language
eng
Summary
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 structures and uncertainties in the data. This monograph is a research book and it is in some sense cross-disciplinary. The methodology itself is firmly rooted in the statistical research tradition, based on probability theory and stochastic processes. However, the methodology has been applied to a problem within physical oceanography, analysing data for significant wave height, which are of crucial importance to ocean engineering disciplines. Indeed, the statistical properties of significant wave height are important for the design, construction and operation of ships and other marine and coastal structures. Furthermore, the book addresses the question of whether climate change has an effect of the ocean wave climate, and if so what these effects might be. Thus, this book is an important contribution to the on-going debate on climate change, its implications and how to adapt to a changing climate, with a particular focus on the maritime industries and the marine environment. This book should be of general interest to anyone with an interest in statistical modelling of environmental processes, and in particular to those with a particular interest in the ocean wave climate. It is written on a level that should be understandable to everyone with a basic background in statistics or elementary mathematics, and an introduction to some basic concepts is given in appendices for the uninitiated reader. The intended readership includes students and professionals involved in statistics, oceanography, ocean engineering, environmental research, climate sciences and risk assessment. Moreover, different stakeholders within the maritime industries such as design offices, classification societies, ship owners, yards and operators, flag states and intergovernmental agencies such as the IMO might find the results relevant
Member of
Cataloging source
GW5XE
http://library.link/vocab/creatorName
Vanem, Erik
Dewey number
551.46/3015118
Illustrations
illustrations
Index
no index present
LC call number
QA927
Literary form
non fiction
Nature of contents
  • dictionaries
  • bibliography
Series statement
Ocean Engineering & Oceanography,
Series volume
volume 2
http://library.link/vocab/subjectName
Water waves
Label
Bayesian hierarchical space-time models with application to significant wave height, Erik Vanem, (electronic book)
Instantiates
Publication
Antecedent source
unknown
Bibliography note
Includes bibliographical references
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Contents
Introduction and Background -- Literature Survey on StochasticWave Models -- A Bayesian Hierarchical Space-Time Model for Significant Wave Height -- Including a Log-Transform of the Data -- Bayesian Hierarchical Modelling of the Ocean Windiness -- Application: Impacts on Ship Structural Loads -- Case study: Modelling the Effect of Climate Change on the World's Oceans -- Summary and Conclusions -- Markov Chain Monte Carlo Methods -- Extreme Value Modelling -- Markov Random Fields -- Derivation of the Full Conditionals of the Bayesian Hierarchical Space-Time Model for Significant Wave Height -- Sampling from a Multi-normal Distribution
Control code
SPR862107560
Dimensions
unknown
Extent
1 online resource (xx, 262 pages)
File format
unknown
Form of item
online
Isbn
9783642302527
Level of compression
unknown
Media category
computer
Media MARC source
rdamedia
Media type code
c
Other control number
10.1007/978-3-642-30253-4
Other physical details
illustrations (some color).
Quality assurance targets
not applicable
Reformatting quality
unknown
Reproduction note
Electronic resource.
Sound
unknown sound
Specific material designation
remote
Label
Bayesian hierarchical space-time models with application to significant wave height, Erik Vanem, (electronic book)
Publication
Antecedent source
unknown
Bibliography note
Includes bibliographical references
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Contents
Introduction and Background -- Literature Survey on StochasticWave Models -- A Bayesian Hierarchical Space-Time Model for Significant Wave Height -- Including a Log-Transform of the Data -- Bayesian Hierarchical Modelling of the Ocean Windiness -- Application: Impacts on Ship Structural Loads -- Case study: Modelling the Effect of Climate Change on the World's Oceans -- Summary and Conclusions -- Markov Chain Monte Carlo Methods -- Extreme Value Modelling -- Markov Random Fields -- Derivation of the Full Conditionals of the Bayesian Hierarchical Space-Time Model for Significant Wave Height -- Sampling from a Multi-normal Distribution
Control code
SPR862107560
Dimensions
unknown
Extent
1 online resource (xx, 262 pages)
File format
unknown
Form of item
online
Isbn
9783642302527
Level of compression
unknown
Media category
computer
Media MARC source
rdamedia
Media type code
c
Other control number
10.1007/978-3-642-30253-4
Other physical details
illustrations (some color).
Quality assurance targets
not applicable
Reformatting quality
unknown
Reproduction note
Electronic resource.
Sound
unknown sound
Specific material designation
remote

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