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The Resource Bayesian Hierarchical SpaceTime Models with Application to Significant Wave Height, by Erik Vanem, (electronic resource)
Bayesian Hierarchical SpaceTime Models with Application to Significant Wave Height, by Erik Vanem, (electronic resource)
Resource Information
The item Bayesian Hierarchical SpaceTime Models with Application to Significant Wave Height, by Erik Vanem, (electronic resource) represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in Boston University Libraries.This item is available to borrow from all library branches.
Resource Information
The item Bayesian Hierarchical SpaceTime Models with Application to Significant Wave Height, by Erik Vanem, (electronic resource) represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in Boston University Libraries.
This item is available to borrow from all library branches.
 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 spacetime 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 crossdisciplinary. 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 ongoing 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
 Language
 eng
 Extent
 XX, 262 p. 85 illus., 14 illus. in color.
 Contents

 Preface
 Acronyms
 1.Introduction and Background
 2.Literature Survey on StochasticWave Models
 3.A Bayesian Hierarchical SpaceTime Model for Significant Wave Height
 4.Including a LogTransform 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 Worldâ€™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 SpaceTime Model for Significant Wave Height
 E.Sampling from a Multinormal Distribution
 Isbn
 9783642302534
 Label
 Bayesian Hierarchical SpaceTime Models with Application to Significant Wave Height
 Title
 Bayesian Hierarchical SpaceTime Models with Application to Significant Wave Height
 Statement of responsibility
 by Erik Vanem
 Subject

 Probability Theory and Stochastic Processes
 Distribution (Probability theory)
 Statistics
 Distribution (Probability theory)
 Electronic resources
 Statistics
 Distribution (Probability theory)
 Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences
 Statistics
 Geophysics and Environmental Physics
 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 spacetime 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 crossdisciplinary. 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 ongoing 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
 http://library.link/vocab/creatorName
 Vanem, Erik
 Image bit depth
 0
 LC call number
 QA276280
 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 SpaceTime Models with Application to Significant Wave Height, by Erik Vanem, (electronic resource)
 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 SpaceTime Model for Significant Wave Height  4.Including a LogTransform 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 Worldâ€™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 SpaceTime Model for Significant Wave Height  E.Sampling from a Multinormal 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
 10.1007/9783642302534
 Other physical details
 online resource.
 Quality assurance targets
 absent
 Reformatting quality
 access
 Specific material designation
 remote
 System control number
 (DEHe213)9783642302534
 Label
 Bayesian Hierarchical SpaceTime Models with Application to Significant Wave Height, by Erik Vanem, (electronic resource)
 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 SpaceTime Model for Significant Wave Height  4.Including a LogTransform 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 Worldâ€™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 SpaceTime Model for Significant Wave Height  E.Sampling from a Multinormal 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
 10.1007/9783642302534
 Other physical details
 online resource.
 Quality assurance targets
 absent
 Reformatting quality
 access
 Specific material designation
 remote
 System control number
 (DEHe213)9783642302534
Subject
 Distribution (Probability theory)
 Distribution (Probability theory)
 Distribution (Probability theory)
 Electronic resources
 Geophysics and Environmental Physics
 Probability Theory and Stochastic Processes
 Statistics
 Statistics
 Statistics
 Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences
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