Spatiotemporal Random Fields

Spatiotemporal Random Fields

Author: George Christakos

Publisher: Elsevier

Published: 2017-07-26

Total Pages: 696

ISBN-13: 0128030321

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Spatiotemporal Random Fields: Theory and Applications, Second Edition, provides readers with a new and updated edition of the text that explores the application of spatiotemporal random field models to problems in ocean, earth, and atmospheric sciences, spatiotemporal statistics, and geostatistics, among others. The new edition features considerable detail of spatiotemporal random field theory, including ordinary and generalized models, as well as space-time homostationary, isostationary and hetrogeneous approaches. Presenting new theoretical and applied results, with particular emphasis on space-time determination and interpretation, spatiotemporal analysis and modeling, random field geometry, random functionals, probability law, and covariance construction techniques, this book highlights the key role of space-time metrics, the physical interpretation of stochastic differential equations, higher-order space-time variability functions, the validity of major theoretical assumptions in real-world practice (covariance positive-definiteness, metric-adequacy etc.), and the emergence of interdisciplinary phenomena in conditions of multi-sourced real-world uncertainty. Contains applications in the form of examples and case studies, providing readers with first-hand experiences Presents an easy to follow narrative which progresses from simple concepts to more challenging ideas Includes significant updates from the previous edition, including a focus on new theoretical and applied results


Book Synopsis Spatiotemporal Random Fields by : George Christakos

Download or read book Spatiotemporal Random Fields written by George Christakos and published by Elsevier. This book was released on 2017-07-26 with total page 696 pages. Available in PDF, EPUB and Kindle. Book excerpt: Spatiotemporal Random Fields: Theory and Applications, Second Edition, provides readers with a new and updated edition of the text that explores the application of spatiotemporal random field models to problems in ocean, earth, and atmospheric sciences, spatiotemporal statistics, and geostatistics, among others. The new edition features considerable detail of spatiotemporal random field theory, including ordinary and generalized models, as well as space-time homostationary, isostationary and hetrogeneous approaches. Presenting new theoretical and applied results, with particular emphasis on space-time determination and interpretation, spatiotemporal analysis and modeling, random field geometry, random functionals, probability law, and covariance construction techniques, this book highlights the key role of space-time metrics, the physical interpretation of stochastic differential equations, higher-order space-time variability functions, the validity of major theoretical assumptions in real-world practice (covariance positive-definiteness, metric-adequacy etc.), and the emergence of interdisciplinary phenomena in conditions of multi-sourced real-world uncertainty. Contains applications in the form of examples and case studies, providing readers with first-hand experiences Presents an easy to follow narrative which progresses from simple concepts to more challenging ideas Includes significant updates from the previous edition, including a focus on new theoretical and applied results


Random Fields for Spatial Data Modeling

Random Fields for Spatial Data Modeling

Author: Dionissios T. Hristopulos

Publisher: Springer Nature

Published: 2020-02-17

Total Pages: 884

ISBN-13: 9402419187

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This book provides an inter-disciplinary introduction to the theory of random fields and its applications. Spatial models and spatial data analysis are integral parts of many scientific and engineering disciplines. Random fields provide a general theoretical framework for the development of spatial models and their applications in data analysis. The contents of the book include topics from classical statistics and random field theory (regression models, Gaussian random fields, stationarity, correlation functions) spatial statistics (variogram estimation, model inference, kriging-based prediction) and statistical physics (fractals, Ising model, simulated annealing, maximum entropy, functional integral representations, perturbation and variational methods). The book also explores links between random fields, Gaussian processes and neural networks used in machine learning. Connections with applied mathematics are highlighted by means of models based on stochastic partial differential equations. An interlude on autoregressive time series provides useful lower-dimensional analogies and a connection with the classical linear harmonic oscillator. Other chapters focus on non-Gaussian random fields and stochastic simulation methods. The book also presents results based on the author’s research on Spartan random fields that were inspired by statistical field theories originating in physics. The equivalence of the one-dimensional Spartan random field model with the classical, linear, damped harmonic oscillator driven by white noise is highlighted. Ideas with potentially significant computational gains for the processing of big spatial data are presented and discussed. The final chapter concludes with a description of the Karhunen-Loève expansion of the Spartan model. The book will appeal to engineers, physicists, and geoscientists whose research involves spatial models or spatial data analysis. Anyone with background in probability and statistics can read at least parts of the book. Some chapters will be easier to understand by readers familiar with differential equations and Fourier transforms.


Book Synopsis Random Fields for Spatial Data Modeling by : Dionissios T. Hristopulos

Download or read book Random Fields for Spatial Data Modeling written by Dionissios T. Hristopulos and published by Springer Nature. This book was released on 2020-02-17 with total page 884 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides an inter-disciplinary introduction to the theory of random fields and its applications. Spatial models and spatial data analysis are integral parts of many scientific and engineering disciplines. Random fields provide a general theoretical framework for the development of spatial models and their applications in data analysis. The contents of the book include topics from classical statistics and random field theory (regression models, Gaussian random fields, stationarity, correlation functions) spatial statistics (variogram estimation, model inference, kriging-based prediction) and statistical physics (fractals, Ising model, simulated annealing, maximum entropy, functional integral representations, perturbation and variational methods). The book also explores links between random fields, Gaussian processes and neural networks used in machine learning. Connections with applied mathematics are highlighted by means of models based on stochastic partial differential equations. An interlude on autoregressive time series provides useful lower-dimensional analogies and a connection with the classical linear harmonic oscillator. Other chapters focus on non-Gaussian random fields and stochastic simulation methods. The book also presents results based on the author’s research on Spartan random fields that were inspired by statistical field theories originating in physics. The equivalence of the one-dimensional Spartan random field model with the classical, linear, damped harmonic oscillator driven by white noise is highlighted. Ideas with potentially significant computational gains for the processing of big spatial data are presented and discussed. The final chapter concludes with a description of the Karhunen-Loève expansion of the Spartan model. The book will appeal to engineers, physicists, and geoscientists whose research involves spatial models or spatial data analysis. Anyone with background in probability and statistics can read at least parts of the book. Some chapters will be easier to understand by readers familiar with differential equations and Fourier transforms.


Random Field Models in Earth Sciences

Random Field Models in Earth Sciences

Author: George Christakos

Publisher: Elsevier

Published: 2013-10-22

Total Pages: 503

ISBN-13: 1483288307

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This book is about modeling as a prinicipal component of scientific investigations. In general terms, modeling is the funamental process of combining intellectual creativity with physical knowledge and mathematical techniques in order to learn the properties of the mechanisms underlying a physical phenomenon and make predictions. The book focuses on a specific class of models, namely, random field models and certain of their physical applications in the context of a stochastic data analysis and processing research program. The term application is considered here in the sense wherein the mathematical random field model is shaping, but is also being shaped by, its objects. This book explores the application of random field models and stochastic data processing to problems in hydrogeology, geostatistics, climate modeling, and oil reservoir engineering, among others Researchers in the geosciences who work with models of natural processes will find discussion of; Spatiotemporal random fields Space transformation Multidimensional estimation Simulation Sampling design Stochastic partial differential equations


Book Synopsis Random Field Models in Earth Sciences by : George Christakos

Download or read book Random Field Models in Earth Sciences written by George Christakos and published by Elsevier. This book was released on 2013-10-22 with total page 503 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is about modeling as a prinicipal component of scientific investigations. In general terms, modeling is the funamental process of combining intellectual creativity with physical knowledge and mathematical techniques in order to learn the properties of the mechanisms underlying a physical phenomenon and make predictions. The book focuses on a specific class of models, namely, random field models and certain of their physical applications in the context of a stochastic data analysis and processing research program. The term application is considered here in the sense wherein the mathematical random field model is shaping, but is also being shaped by, its objects. This book explores the application of random field models and stochastic data processing to problems in hydrogeology, geostatistics, climate modeling, and oil reservoir engineering, among others Researchers in the geosciences who work with models of natural processes will find discussion of; Spatiotemporal random fields Space transformation Multidimensional estimation Simulation Sampling design Stochastic partial differential equations


Advanced Spatial Modeling with Stochastic Partial Differential Equations Using R and INLA

Advanced Spatial Modeling with Stochastic Partial Differential Equations Using R and INLA

Author: Elias T. Krainski

Publisher: CRC Press

Published: 2018-12-07

Total Pages: 284

ISBN-13: 0429629850

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Modeling spatial and spatio-temporal continuous processes is an important and challenging problem in spatial statistics. Advanced Spatial Modeling with Stochastic Partial Differential Equations Using R and INLA describes in detail the stochastic partial differential equations (SPDE) approach for modeling continuous spatial processes with a Matérn covariance, which has been implemented using the integrated nested Laplace approximation (INLA) in the R-INLA package. Key concepts about modeling spatial processes and the SPDE approach are explained with examples using simulated data and real applications. This book has been authored by leading experts in spatial statistics, including the main developers of the INLA and SPDE methodologies and the R-INLA package. It also includes a wide range of applications: * Spatial and spatio-temporal models for continuous outcomes * Analysis of spatial and spatio-temporal point patterns * Coregionalization spatial and spatio-temporal models * Measurement error spatial models * Modeling preferential sampling * Spatial and spatio-temporal models with physical barriers * Survival analysis with spatial effects * Dynamic space-time regression * Spatial and spatio-temporal models for extremes * Hurdle models with spatial effects * Penalized Complexity priors for spatial models All the examples in the book are fully reproducible. Further information about this book, as well as the R code and datasets used, is available from the book website at http://www.r-inla.org/spde-book. The tools described in this book will be useful to researchers in many fields such as biostatistics, spatial statistics, environmental sciences, epidemiology, ecology and others. Graduate and Ph.D. students will also find this book and associated files a valuable resource to learn INLA and the SPDE approach for spatial modeling.


Book Synopsis Advanced Spatial Modeling with Stochastic Partial Differential Equations Using R and INLA by : Elias T. Krainski

Download or read book Advanced Spatial Modeling with Stochastic Partial Differential Equations Using R and INLA written by Elias T. Krainski and published by CRC Press. This book was released on 2018-12-07 with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modeling spatial and spatio-temporal continuous processes is an important and challenging problem in spatial statistics. Advanced Spatial Modeling with Stochastic Partial Differential Equations Using R and INLA describes in detail the stochastic partial differential equations (SPDE) approach for modeling continuous spatial processes with a Matérn covariance, which has been implemented using the integrated nested Laplace approximation (INLA) in the R-INLA package. Key concepts about modeling spatial processes and the SPDE approach are explained with examples using simulated data and real applications. This book has been authored by leading experts in spatial statistics, including the main developers of the INLA and SPDE methodologies and the R-INLA package. It also includes a wide range of applications: * Spatial and spatio-temporal models for continuous outcomes * Analysis of spatial and spatio-temporal point patterns * Coregionalization spatial and spatio-temporal models * Measurement error spatial models * Modeling preferential sampling * Spatial and spatio-temporal models with physical barriers * Survival analysis with spatial effects * Dynamic space-time regression * Spatial and spatio-temporal models for extremes * Hurdle models with spatial effects * Penalized Complexity priors for spatial models All the examples in the book are fully reproducible. Further information about this book, as well as the R code and datasets used, is available from the book website at http://www.r-inla.org/spde-book. The tools described in this book will be useful to researchers in many fields such as biostatistics, spatial statistics, environmental sciences, epidemiology, ecology and others. Graduate and Ph.D. students will also find this book and associated files a valuable resource to learn INLA and the SPDE approach for spatial modeling.


Spatiotemporal Analysis of Extreme Hydrological Events

Spatiotemporal Analysis of Extreme Hydrological Events

Author: Gerald Corzo

Publisher: Elsevier

Published: 2018-11-20

Total Pages: 194

ISBN-13: 0128117311

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Spatio-temporal Analysis of Extreme Hydrological Events offers an extensive view of the experiences and applications of the latest developments and methodologies for analyzing and understanding extreme environmental and hydrological events. The book addresses the topic using spatio-temporal methods, such as space-time geostatistics, machine learning, statistical theory, hydrological modelling, neural network and evolutionary algorithms. This important resource for both hydrologists and statisticians interested in the framework of spatial and temporal analysis of hydrological events will provide users with an enhanced understanding of the relationship between magnitude, dynamics and the probability of extreme hydrological events. Presents spatio-temporal processes, including multivariate dynamic modelling Provides varying methodological approaches, giving the readers multiple hydrological modelling information to use in their work Includes a variety of case studies making the context of the book relatable to everyday working situations


Book Synopsis Spatiotemporal Analysis of Extreme Hydrological Events by : Gerald Corzo

Download or read book Spatiotemporal Analysis of Extreme Hydrological Events written by Gerald Corzo and published by Elsevier. This book was released on 2018-11-20 with total page 194 pages. Available in PDF, EPUB and Kindle. Book excerpt: Spatio-temporal Analysis of Extreme Hydrological Events offers an extensive view of the experiences and applications of the latest developments and methodologies for analyzing and understanding extreme environmental and hydrological events. The book addresses the topic using spatio-temporal methods, such as space-time geostatistics, machine learning, statistical theory, hydrological modelling, neural network and evolutionary algorithms. This important resource for both hydrologists and statisticians interested in the framework of spatial and temporal analysis of hydrological events will provide users with an enhanced understanding of the relationship between magnitude, dynamics and the probability of extreme hydrological events. Presents spatio-temporal processes, including multivariate dynamic modelling Provides varying methodological approaches, giving the readers multiple hydrological modelling information to use in their work Includes a variety of case studies making the context of the book relatable to everyday working situations


Statistics for Spatio-Temporal Data

Statistics for Spatio-Temporal Data

Author: Noel Cressie

Publisher: John Wiley & Sons

Published: 2015-11-02

Total Pages: 624

ISBN-13: 1119243041

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Winner of the 2013 DeGroot Prize. A state-of-the-art presentation of spatio-temporal processes,bridging classic ideas with modern hierarchical statisticalmodeling concepts and the latest computational methods Noel Cressie and Christopher K. Wikle, are also winnersof the 2011 PROSE Award in the Mathematics category, for thebook “Statistics for Spatio-Temporal Data” (2011),published by John Wiley and Sons. (The PROSE awards, forProfessional and Scholarly Excellence, are given by the Associationof American Publishers, the national trade association of the USbook publishing industry.) Statistics for Spatio-Temporal Data has now beenreprinted with small corrections to the text andthe bibliography. The overall content and pagination of thenew printing remains the same; the difference comes inthe form of corrections to typographical errors, editing ofincomplete and missing references, and some updated spatio-temporalinterpretations. From understanding environmental processes and climate trends todeveloping new technologies for mapping public-health data and thespread of invasive-species, there is a high demand for statisticalanalyses of data that take spatial, temporal, and spatio-temporalinformation into account. Statistics for Spatio-TemporalData presents a systematic approach to key quantitativetechniques that incorporate the latest advances in statisticalcomputing as well as hierarchical, particularly Bayesian,statistical modeling, with an emphasis on dynamical spatio-temporalmodels. Cressie and Wikle supply a unique presentation thatincorporates ideas from the areas of time series and spatialstatistics as well as stochastic processes. Beginning with separatetreatments of temporal data and spatial data, the book combinesthese concepts to discuss spatio-temporal statistical methods forunderstanding complex processes. Topics of coverage include: Exploratory methods for spatio-temporal data, includingvisualization, spectral analysis, empirical orthogonal functionanalysis, and LISAs Spatio-temporal covariance functions, spatio-temporal kriging,and time series of spatial processes Development of hierarchical dynamical spatio-temporal models(DSTMs), with discussion of linear and nonlinear DSTMs andcomputational algorithms for their implementation Quantifying and exploring spatio-temporal variability inscientific applications, including case studies based on real-worldenvironmental data Throughout the book, interesting applications demonstrate therelevance of the presented concepts. Vivid, full-color graphicsemphasize the visual nature of the topic, and a related FTP sitecontains supplementary material. Statistics for Spatio-TemporalData is an excellent book for a graduate-level course onspatio-temporal statistics. It is also a valuable reference forresearchers and practitioners in the fields of applied mathematics,engineering, and the environmental and health sciences.


Book Synopsis Statistics for Spatio-Temporal Data by : Noel Cressie

Download or read book Statistics for Spatio-Temporal Data written by Noel Cressie and published by John Wiley & Sons. This book was released on 2015-11-02 with total page 624 pages. Available in PDF, EPUB and Kindle. Book excerpt: Winner of the 2013 DeGroot Prize. A state-of-the-art presentation of spatio-temporal processes,bridging classic ideas with modern hierarchical statisticalmodeling concepts and the latest computational methods Noel Cressie and Christopher K. Wikle, are also winnersof the 2011 PROSE Award in the Mathematics category, for thebook “Statistics for Spatio-Temporal Data” (2011),published by John Wiley and Sons. (The PROSE awards, forProfessional and Scholarly Excellence, are given by the Associationof American Publishers, the national trade association of the USbook publishing industry.) Statistics for Spatio-Temporal Data has now beenreprinted with small corrections to the text andthe bibliography. The overall content and pagination of thenew printing remains the same; the difference comes inthe form of corrections to typographical errors, editing ofincomplete and missing references, and some updated spatio-temporalinterpretations. From understanding environmental processes and climate trends todeveloping new technologies for mapping public-health data and thespread of invasive-species, there is a high demand for statisticalanalyses of data that take spatial, temporal, and spatio-temporalinformation into account. Statistics for Spatio-TemporalData presents a systematic approach to key quantitativetechniques that incorporate the latest advances in statisticalcomputing as well as hierarchical, particularly Bayesian,statistical modeling, with an emphasis on dynamical spatio-temporalmodels. Cressie and Wikle supply a unique presentation thatincorporates ideas from the areas of time series and spatialstatistics as well as stochastic processes. Beginning with separatetreatments of temporal data and spatial data, the book combinesthese concepts to discuss spatio-temporal statistical methods forunderstanding complex processes. Topics of coverage include: Exploratory methods for spatio-temporal data, includingvisualization, spectral analysis, empirical orthogonal functionanalysis, and LISAs Spatio-temporal covariance functions, spatio-temporal kriging,and time series of spatial processes Development of hierarchical dynamical spatio-temporal models(DSTMs), with discussion of linear and nonlinear DSTMs andcomputational algorithms for their implementation Quantifying and exploring spatio-temporal variability inscientific applications, including case studies based on real-worldenvironmental data Throughout the book, interesting applications demonstrate therelevance of the presented concepts. Vivid, full-color graphicsemphasize the visual nature of the topic, and a related FTP sitecontains supplementary material. Statistics for Spatio-TemporalData is an excellent book for a graduate-level course onspatio-temporal statistics. It is also a valuable reference forresearchers and practitioners in the fields of applied mathematics,engineering, and the environmental and health sciences.


Spatio-Temporal Statistics with R

Spatio-Temporal Statistics with R

Author: Christopher K. Wikle

Publisher: CRC Press

Published: 2019-02-18

Total Pages: 380

ISBN-13: 0429649789

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The world is becoming increasingly complex, with larger quantities of data available to be analyzed. It so happens that much of these "big data" that are available are spatio-temporal in nature, meaning that they can be indexed by their spatial locations and time stamps. Spatio-Temporal Statistics with R provides an accessible introduction to statistical analysis of spatio-temporal data, with hands-on applications of the statistical methods using R Labs found at the end of each chapter. The book: Gives a step-by-step approach to analyzing spatio-temporal data, starting with visualization, then statistical modelling, with an emphasis on hierarchical statistical models and basis function expansions, and finishing with model evaluation Provides a gradual entry to the methodological aspects of spatio-temporal statistics Provides broad coverage of using R as well as "R Tips" throughout. Features detailed examples and applications in end-of-chapter Labs Features "Technical Notes" throughout to provide additional technical detail where relevant Supplemented by a website featuring the associated R package, data, reviews, errata, a discussion forum, and more The book fills a void in the literature and available software, providing a bridge for students and researchers alike who wish to learn the basics of spatio-temporal statistics. It is written in an informal style and functions as a down-to-earth introduction to the subject. Any reader familiar with calculus-based probability and statistics, and who is comfortable with basic matrix-algebra representations of statistical models, would find this book easy to follow. The goal is to give as many people as possible the tools and confidence to analyze spatio-temporal data.


Book Synopsis Spatio-Temporal Statistics with R by : Christopher K. Wikle

Download or read book Spatio-Temporal Statistics with R written by Christopher K. Wikle and published by CRC Press. This book was released on 2019-02-18 with total page 380 pages. Available in PDF, EPUB and Kindle. Book excerpt: The world is becoming increasingly complex, with larger quantities of data available to be analyzed. It so happens that much of these "big data" that are available are spatio-temporal in nature, meaning that they can be indexed by their spatial locations and time stamps. Spatio-Temporal Statistics with R provides an accessible introduction to statistical analysis of spatio-temporal data, with hands-on applications of the statistical methods using R Labs found at the end of each chapter. The book: Gives a step-by-step approach to analyzing spatio-temporal data, starting with visualization, then statistical modelling, with an emphasis on hierarchical statistical models and basis function expansions, and finishing with model evaluation Provides a gradual entry to the methodological aspects of spatio-temporal statistics Provides broad coverage of using R as well as "R Tips" throughout. Features detailed examples and applications in end-of-chapter Labs Features "Technical Notes" throughout to provide additional technical detail where relevant Supplemented by a website featuring the associated R package, data, reviews, errata, a discussion forum, and more The book fills a void in the literature and available software, providing a bridge for students and researchers alike who wish to learn the basics of spatio-temporal statistics. It is written in an informal style and functions as a down-to-earth introduction to the subject. Any reader familiar with calculus-based probability and statistics, and who is comfortable with basic matrix-algebra representations of statistical models, would find this book easy to follow. The goal is to give as many people as possible the tools and confidence to analyze spatio-temporal data.


Spatio-Temporal Methods in Environmental Epidemiology

Spatio-Temporal Methods in Environmental Epidemiology

Author: Gavin Shaddick

Publisher: CRC Press

Published: 2015-06-17

Total Pages: 383

ISBN-13: 1482237040

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Teaches Students How to Perform Spatio-Temporal Analyses within Epidemiological StudiesSpatio-Temporal Methods in Environmental Epidemiology is the first book of its kind to specifically address the interface between environmental epidemiology and spatio-temporal modeling. In response to the growing need for collaboration between statisticians and


Book Synopsis Spatio-Temporal Methods in Environmental Epidemiology by : Gavin Shaddick

Download or read book Spatio-Temporal Methods in Environmental Epidemiology written by Gavin Shaddick and published by CRC Press. This book was released on 2015-06-17 with total page 383 pages. Available in PDF, EPUB and Kindle. Book excerpt: Teaches Students How to Perform Spatio-Temporal Analyses within Epidemiological StudiesSpatio-Temporal Methods in Environmental Epidemiology is the first book of its kind to specifically address the interface between environmental epidemiology and spatio-temporal modeling. In response to the growing need for collaboration between statisticians and


Graphical Models, Exponential Families, and Variational Inference

Graphical Models, Exponential Families, and Variational Inference

Author: Martin J. Wainwright

Publisher: Now Publishers Inc

Published: 2008

Total Pages: 324

ISBN-13: 1601981848

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The core of this paper is a general set of variational principles for the problems of computing marginal probabilities and modes, applicable to multivariate statistical models in the exponential family.


Book Synopsis Graphical Models, Exponential Families, and Variational Inference by : Martin J. Wainwright

Download or read book Graphical Models, Exponential Families, and Variational Inference written by Martin J. Wainwright and published by Now Publishers Inc. This book was released on 2008 with total page 324 pages. Available in PDF, EPUB and Kindle. Book excerpt: The core of this paper is a general set of variational principles for the problems of computing marginal probabilities and modes, applicable to multivariate statistical models in the exponential family.


Modern Spatiotemporal Geostatistics

Modern Spatiotemporal Geostatistics

Author: George Christakos

Publisher: Courier Corporation

Published: 2013-09-26

Total Pages: 305

ISBN-13: 0486310930

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This scholarly introductory treatment explores the fundamentals of modern geostatistics, viewing them as the product of the advancement of the epistemic status of stochastic data analysis. The book's main focus is the Bayesian maximum entropy approach for studying spatiotemporal distributions of natural variables, an approach that offers readers a deeper understanding of the role of geostatistics in improved mathematical models of scientific mapping. Starting with a overview of the uses of spatiotemporal mapping in the natural sciences, the text explores spatiotemporal geometry, the epistemic paradigm, the mathematical formulation of the Bayesian maximum entropy method, and analytical expressions of the posterior operator. Additional topics include uncertainty assessment, single- and multi-point analytical formulations, and popular methods. An innovative contribution to the field of space and time analysis, this volume offers many potential applications in epidemiology, geography, biology, and other fields.


Book Synopsis Modern Spatiotemporal Geostatistics by : George Christakos

Download or read book Modern Spatiotemporal Geostatistics written by George Christakos and published by Courier Corporation. This book was released on 2013-09-26 with total page 305 pages. Available in PDF, EPUB and Kindle. Book excerpt: This scholarly introductory treatment explores the fundamentals of modern geostatistics, viewing them as the product of the advancement of the epistemic status of stochastic data analysis. The book's main focus is the Bayesian maximum entropy approach for studying spatiotemporal distributions of natural variables, an approach that offers readers a deeper understanding of the role of geostatistics in improved mathematical models of scientific mapping. Starting with a overview of the uses of spatiotemporal mapping in the natural sciences, the text explores spatiotemporal geometry, the epistemic paradigm, the mathematical formulation of the Bayesian maximum entropy method, and analytical expressions of the posterior operator. Additional topics include uncertainty assessment, single- and multi-point analytical formulations, and popular methods. An innovative contribution to the field of space and time analysis, this volume offers many potential applications in epidemiology, geography, biology, and other fields.