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This book provides an example of a thorough statistical treatment of ocean wave data in space and time. It demonstrates 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 partly cross-disciplinary. The methodology itself is firmly rooted in the statistical research tradition, based on probability theory and stochastic processes. However, that methodology has been applied to a problem in the field of physical oceanography, analyzing data for significant wave height, which is 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 that effect 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 value to anyone with an interest in the statistical modelling of environmental processes, and in particular to those with an 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 provided in the 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, the book’s findings are relevant for various stakeholders in the maritime industries such as design offices, classification societies, ship owners, yards and operators, flag states and intergovernmental agencies such as the IMO.
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This book consists of 44 technical papers presented at the Ninth International Geostatistics Congress held in Oslo, Norway in June 2012. The papers have been reviewed by a panel of specialists in Geostatistics. The book is divided into four main sections: Theory; Petroleum; Mining; and Environment, Climate and Hydrology. The first section focuses on new ideas of general interest to many fields of applications. The next sections are more focused on the particular needs of the particular industry or activity. Geostatistics is vital to any industry dependent on natural resources. Methods from geostatistics are used for estimating reserves, quantifying economical risk and planning of future industrial operations. Geostatistics is also an important tool for mapping environmental hazard and integrating climate data.
The Earth is the only planet in our solar system that supports life. The complex process of evolution occurred on Earth only because of some unique environmental conditions that were present: water, an oxygen-rich atmosphere, and a suitable surface temperature. Climate change refers to a statistically significant variation in either the mean state of the climate or in its variability, persisting for an extended period (typically decades or longer). Thus, it requires field of attention towards reduction in the rise in atmospheric temperature, by controlling emitted greenhouse gases into the atmosphere in order to preserve natural resources and by introducing new technologies on alternative fuels. This book presents the fundamental effect on the origin of climate change, impacts over ice cap, melting of Arctic ice, rise in sea level and related technologies that can be implemented to cultivate our land for agriculture, growing forestation to reduce the impact of temperature rise and disaster on human being as well as on other livelihood. This also need to strive for novel policies that world leader should adopt.
This important, self-contained reference deals with structural life assessment (SLA) and structural health monitoring (SHM) in a combined form. SLA periodically evaluates the state and condition of a structural system and provides recommendations for possible maintenance actions or the end of structural service life. It is a diversified field and relies on the theories of fracture mechanics, fatigue damage process, and reliability theory. For common structures, their life assessment is not only governed by the theory of fracture mechanics and fatigue damage process, but by other factors such as corrosion, grounding, and sudden collision. On the other hand, SHM deals with the detection, prediction, and location of crack development online. Both SLA and SHM are combined in a unified and coherent treatment.
Extreme Value Modeling and Risk Analysis: Methods and Applications presents a broad overview of statistical modeling of extreme events along with the most recent methodologies and various applications. The book brings together background material and advanced topics, eliminating the need to sort through the massive amount of literature on the subje
Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors—all leaders in the statistics community—introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice. New to the Third Edition Four new chapters on nonparametric modeling Coverage of weakly informative priors and boundary-avoiding priors Updated discussion of cross-validation and predictive information criteria Improved convergence monitoring and effective sample size calculations for iterative simulation Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation New and revised software code The book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book’s web page.
This book introduces readers to various types of offshore platform geometries. It addresses the various environmental loads encountered by these structures, and provides detailed descriptions of the fundamentals of structural dynamics in a classroom style, helping readers estimate damping in offshore structures and grasp these aspects’ applications in preliminary analysis and design. Basic concepts of structural dynamics are emphasized through simple illustrative examples and exercises. Design methodologies and guidelines, which are FORM based concepts, are explained through a selection of applied sample structures. Each chapter also features tutorials and exercises for self-learning. A dedicated chapter on stochastic dynamics helps students to extend the basic concepts of structural dynamics to this advanced domain of research. Hydrodynamic response of offshore structures with perforated members is one of the most recent research applications, and has proven to be one of the most effective means of retrofitting offshore structures. In addition, the book integrates the concepts of structural dynamics with the FORM-evolved design of offshore structures, offering a unique approach. This new edition is divided into seven chapters, each of which has been updated. Each chapter also includes a section on frequently asked Questions and Answers (Q&A), which enhances understanding of this complex subject through easy and self-explanatory text. Furthermore, the book presents valuable content with respect to new and recent research carried out by the author in structural dynamics. All numeric examples have been re-checked with more additional explanations. New exercises have been added to improve understanding of the subject matter. Computer coding is also included (wherever possible) to aid computer-based learning of the contents of the book. The book can serve as a textbook for senior undergraduate and graduate courses in civil, structural, applied mechanics, mechanical, aerospace, naval architecture and ocean engineering programs. The book can also serve as a text for professional learning and development programs or as a guide for practicing and consulting offshore structural engineers. The contents of this book will be useful to graduate students, researchers, and professionals alike.
An intermediate-level treatment of Bayesian hierarchical models and their applications, this book demonstrates the advantages of a Bayesian approach to data sets involving inferences for collections of related units or variables, and in methods where parameters can be treated as random collections. Through illustrative data analysis and attention to statistical computing, this book facilitates practical implementation of Bayesian hierarchical methods. The new edition is a revision of the book Applied Bayesian Hierarchical Methods. It maintains a focus on applied modelling and data analysis, but now using entirely R-based Bayesian computing options. It has been updated with a new chapter on regression for causal effects, and one on computing options and strategies. This latter chapter is particularly important, due to recent advances in Bayesian computing and estimation, including the development of rjags and rstan. It also features updates throughout with new examples. The examples exploit and illustrate the broader advantages of the R computing environment, while allowing readers to explore alternative likelihood assumptions, regression structures, and assumptions on prior densities. Features: Provides a comprehensive and accessible overview of applied Bayesian hierarchical modelling Includes many real data examples to illustrate different modelling topics R code (based on rjags, jagsUI, R2OpenBUGS, and rstan) is integrated into the book, emphasizing implementation Software options and coding principles are introduced in new chapter on computing Programs and data sets available on the book’s website