The Resource Bayesian Hierarchical Space-Time Models with Application to Significant Wave Height, by Erik Vanem, (electronic resource)

Bayesian Hierarchical Space-Time Models with Application to Significant Wave Height, by Erik Vanem, (electronic resource)

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
by Erik Vanem
Creator
Contributor
Author
Provider
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 incudes 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
http://library.link/vocab/creatorName
Vanem, Erik
Image bit depth
0
LC call number
QA276-280
Literary form
non fiction
http://library.link/vocab/relatedWorkOrContributorName
SpringerLink
Series statement
Ocean Engineering & Oceanography,
Series volume
2
http://library.link/vocab/subjectName
  • Statistics
  • Distribution (Probability theory)
  • Statistics
  • Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences
  • Probability Theory and Stochastic Processes
  • Geophysics and Environmental Physics
Label
Bayesian Hierarchical Space-Time Models with Application to Significant Wave Height, by Erik Vanem, (electronic resource)
Instantiates
Publication
Antecedent source
mixed
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Color
not applicable
Content category
text
Content type code
txt
Content type MARC source
rdacontent
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 World’s 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
Dimensions
unknown
Extent
XX, 262 p. 85 illus., 14 illus. in color.
File format
multiple file formats
Form of item
electronic
Isbn
9783642302534
Level of compression
uncompressed
Media category
computer
Media MARC source
rdamedia
Media type code
c
Other control number
10.1007/978-3-642-30253-4
Other physical details
online resource.
Quality assurance targets
absent
Reformatting quality
access
Specific material designation
remote
System control number
(DE-He213)978-3-642-30253-4
Label
Bayesian Hierarchical Space-Time Models with Application to Significant Wave Height, by Erik Vanem, (electronic resource)
Publication
Antecedent source
mixed
Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Color
not applicable
Content category
text
Content type code
txt
Content type MARC source
rdacontent
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 World’s 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
Dimensions
unknown
Extent
XX, 262 p. 85 illus., 14 illus. in color.
File format
multiple file formats
Form of item
electronic
Isbn
9783642302534
Level of compression
uncompressed
Media category
computer
Media MARC source
rdamedia
Media type code
c
Other control number
10.1007/978-3-642-30253-4
Other physical details
online resource.
Quality assurance targets
absent
Reformatting quality
access
Specific material designation
remote
System control number
(DE-He213)978-3-642-30253-4

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