9781032508559 - Hierarchical Modeling and Analysis for Spatial Data (Chapman & Hall/CRC Monographs on Statistics and Applied Probability) de Banerjee, Sudipto; Gelfand, Alan E.; Ca... (23 resultados)

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Hardback. Condición: New. Hierarchical Modeling and Analysis for Spatial Data, Third Edition is the latest edition of this popular and authoritative text on Bayesian modeling and inference for spatial and spatial-temporal data. The text presents a comprehensive and up-to-date treatment of hierarchical and multilevel modeling for spatial and spatio-temporal data within a Bayesian framework. Over the past decade since the second edition, spatial statistics has evolved significantly driven by an explosion in data availability and advances in Bayesian computation. This edition reflects those changes, introducing new methods, expanded applications, and enhanced computational resources to support researchers and practitioners across disciplines, including environmental science, ecology, and public health.Key features of the third edition:A dedicated chapter on state-of-the-art Bayesian modeling of large spatial and spatio-temporal datasetsTwo new chapters on spatial point pattern analysis, covering both foundational and Bayesian perspectivesA new chapter on spatial data fusion, integrating diverse spatial data sources from different probabilistic mechanismsAn accessible introduction to GPS mapping, geodesic distances, and mathematical cartographyAn expanded special topics chapter, including spatial challenges with finite population modeling and spatial directional dataA thoroughly revised chapter on Bayesian inference, featuring an updated review of modern computational techniquesA dedicated GitHub repository providing R programs and solutions to selected exercises, ensuring continued access to evolving software developmentsWith refreshed content throughout, this edition serves as an essential reference for statisticians, data scientists, and researchers working with spatial data. Graduate students and professionals seeking a deep understanding of Bayesian spatial modeling will find this volume an invaluable resource for both theory and practice.

Hardback. Condición: New. Hierarchical Modeling and Analysis for Spatial Data, Third Edition is the latest edition of this popular and authoritative text on Bayesian modeling and inference for spatial and spatial-temporal data. The text presents a comprehensive and up-to-date treatment of hierarchical and multilevel modeling for spatial and spatio-temporal data within a Bayesian framework. Over the past decade since the second edition, spatial statistics has evolved significantly driven by an explosion in data availability and advances in Bayesian computation. This edition reflects those changes, introducing new methods, expanded applications, and enhanced computational resources to support researchers and practitioners across disciplines, including environmental science, ecology, and public health.Key features of the third edition:A dedicated chapter on state-of-the-art Bayesian modeling of large spatial and spatio-temporal datasetsTwo new chapters on spatial point pattern analysis, covering both foundational and Bayesian perspectivesA new chapter on spatial data fusion, integrating diverse spatial data sources from different probabilistic mechanismsAn accessible introduction to GPS mapping, geodesic distances, and mathematical cartographyAn expanded special topics chapter, including spatial challenges with finite population modeling and spatial directional dataA thoroughly revised chapter on Bayesian inference, featuring an updated review of modern computational techniquesA dedicated GitHub repository providing R programs and solutions to selected exercises, ensuring continued access to evolving software developmentsWith refreshed content throughout, this edition serves as an essential reference for statisticians, data scientists, and researchers working with spatial data. Graduate students and professionals seeking a deep understanding of Bayesian spatial modeling will find this volume an invaluable resource for both theory and practice.

Hardcover. Condición: Brand New. 3rd edition. 696 pages. 10.00x7.00x10.00 inches. In Stock.

Hardback. Condición: New. Hierarchical Modeling and Analysis for Spatial Data, Third Edition is the latest edition of this popular and authoritative text on Bayesian modeling and inference for spatial and spatial-temporal data. The text presents a comprehensive and up-to-date treatment of hierarchical and multilevel modeling for spatial and spatio-temporal data within a Bayesian framework. Over the past decade since the second edition, spatial statistics has evolved significantly driven by an explosion in data availability and advances in Bayesian computation. This edition reflects those changes, introducing new methods, expanded applications, and enhanced computational resources to support researchers and practitioners across disciplines, including environmental science, ecology, and public health.Key features of the third edition:A dedicated chapter on state-of-the-art Bayesian modeling of large spatial and spatio-temporal datasetsTwo new chapters on spatial point pattern analysis, covering both foundational and Bayesian perspectivesA new chapter on spatial data fusion, integrating diverse spatial data sources from different probabilistic mechanismsAn accessible introduction to GPS mapping, geodesic distances, and mathematical cartographyAn expanded special topics chapter, including spatial challenges with finite population modeling and spatial directional dataA thoroughly revised chapter on Bayesian inference, featuring an updated review of modern computational techniquesA dedicated GitHub repository providing R programs and solutions to selected exercises, ensuring continued access to evolving software developmentsWith refreshed content throughout, this edition serves as an essential reference for statisticians, data scientists, and researchers working with spatial data. Graduate students and professionals seeking a deep understanding of Bayesian spatial modeling will find this volume an invaluable resource for both theory and practice.

Hardback. Condición: New. Hierarchical Modeling and Analysis for Spatial Data, Third Edition is the latest edition of this popular and authoritative text on Bayesian modeling and inference for spatial and spatial-temporal data. The text presents a comprehensive and up-to-date treatment of hierarchical and multilevel modeling for spatial and spatio-temporal data within a Bayesian framework. Over the past decade since the second edition, spatial statistics has evolved significantly driven by an explosion in data availability and advances in Bayesian computation. This edition reflects those changes, introducing new methods, expanded applications, and enhanced computational resources to support researchers and practitioners across disciplines, including environmental science, ecology, and public health.Key features of the third edition:A dedicated chapter on state-of-the-art Bayesian modeling of large spatial and spatio-temporal datasetsTwo new chapters on spatial point pattern analysis, covering both foundational and Bayesian perspectivesA new chapter on spatial data fusion, integrating diverse spatial data sources from different probabilistic mechanismsAn accessible introduction to GPS mapping, geodesic distances, and mathematical cartographyAn expanded special topics chapter, including spatial challenges with finite population modeling and spatial directional dataA thoroughly revised chapter on Bayesian inference, featuring an updated review of modern computational techniquesA dedicated GitHub repository providing R programs and solutions to selected exercises, ensuring continued access to evolving software developmentsWith refreshed content throughout, this edition serves as an essential reference for statisticians, data scientists, and researchers working with spatial data. Graduate students and professionals seeking a deep understanding of Bayesian spatial modeling will find this volume an invaluable resource for both theory and practice.

Hardcover. Condición: new. Hardcover. Hierarchical Modeling and Analysis for Spatial Data, Third Edition is the latest edition of this popular and authoritative text on Bayesian modeling and inference for spatial and spatial-temporal data. The text presents a comprehensive and up-to-date treatment of hierarchical and multilevel modeling for spatial and spatio-temporal data within a Bayesian framework. Over the past decade since the second edition, spatial statistics has evolved significantly driven by an explosion in data availability and advances in Bayesian computation. This edition reflects those changes, introducing new methods, expanded applications, and enhanced computational resources to support researchers and practitioners across disciplines, including environmental science, ecology, and public health.Key features of the third edition:A dedicated chapter on state-of-the-art Bayesian modeling of large spatial and spatio-temporal datasetsTwo new chapters on spatial point pattern analysis, covering both foundational and Bayesian perspectivesA new chapter on spatial data fusion, integrating diverse spatial data sources from different probabilistic mechanismsAn accessible introduction to GPS mapping, geodesic distances, and mathematical cartographyAn expanded special topics chapter, including spatial challenges with finite population modeling and spatial directional dataA thoroughly revised chapter on Bayesian inference, featuring an updated review of modern computational techniquesA dedicated GitHub repository providing R programs and solutions to selected exercises, ensuring continued access to evolving software developmentsWith refreshed content throughout, this edition serves as an essential reference for statisticians, data scientists, and researchers working with spatial data. Graduate students and professionals seeking a deep understanding of Bayesian spatial modeling will find this volume an invaluable resource for both theory and practice. The 3rd edition of this popular and authoritative text on Bayesian modeling and inference for spatial and spatial-temporal data presents a comprehensive presents a comprehensive and up-to-date treatment of hierarchical and multilevel modeling for spatial and spatio-temporal data within a Bayesian framework. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

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Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Alan E. Gelfand is The James B Duke Professor Emeritus of Statistical Science at Duke University. He also enjoys a secondary appointment as Professor of Environmental Science and Policy in the Nicholas School. Author of more than 330 papers and 6 .

Hardcover. Condición: new. Hardcover. Hierarchical Modeling and Analysis for Spatial Data, Third Edition is the latest edition of this popular and authoritative text on Bayesian modeling and inference for spatial and spatial-temporal data. The text presents a comprehensive and up-to-date treatment of hierarchical and multilevel modeling for spatial and spatio-temporal data within a Bayesian framework. Over the past decade since the second edition, spatial statistics has evolved significantly driven by an explosion in data availability and advances in Bayesian computation. This edition reflects those changes, introducing new methods, expanded applications, and enhanced computational resources to support researchers and practitioners across disciplines, including environmental science, ecology, and public health.Key features of the third edition:A dedicated chapter on state-of-the-art Bayesian modeling of large spatial and spatio-temporal datasetsTwo new chapters on spatial point pattern analysis, covering both foundational and Bayesian perspectivesA new chapter on spatial data fusion, integrating diverse spatial data sources from different probabilistic mechanismsAn accessible introduction to GPS mapping, geodesic distances, and mathematical cartographyAn expanded special topics chapter, including spatial challenges with finite population modeling and spatial directional dataA thoroughly revised chapter on Bayesian inference, featuring an updated review of modern computational techniquesA dedicated GitHub repository providing R programs and solutions to selected exercises, ensuring continued access to evolving software developmentsWith refreshed content throughout, this edition serves as an essential reference for statisticians, data scientists, and researchers working with spatial data. Graduate students and professionals seeking a deep understanding of Bayesian spatial modeling will find this volume an invaluable resource for both theory and practice. The 3rd edition of this popular and authoritative text on Bayesian modeling and inference for spatial and spatial-temporal data presents a comprehensive presents a comprehensive and up-to-date treatment of hierarchical and multilevel modeling for spatial and spatio-temporal data within a Bayesian framework. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

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Hardcover. Condición: new. Hardcover. Hierarchical Modeling and Analysis for Spatial Data, Third Edition is the latest edition of this popular and authoritative text on Bayesian modeling and inference for spatial and spatial-temporal data. The text presents a comprehensive and up-to-date treatment of hierarchical and multilevel modeling for spatial and spatio-temporal data within a Bayesian framework. Over the past decade since the second edition, spatial statistics has evolved significantly driven by an explosion in data availability and advances in Bayesian computation. This edition reflects those changes, introducing new methods, expanded applications, and enhanced computational resources to support researchers and practitioners across disciplines, including environmental science, ecology, and public health.Key features of the third edition:A dedicated chapter on state-of-the-art Bayesian modeling of large spatial and spatio-temporal datasetsTwo new chapters on spatial point pattern analysis, covering both foundational and Bayesian perspectivesA new chapter on spatial data fusion, integrating diverse spatial data sources from different probabilistic mechanismsAn accessible introduction to GPS mapping, geodesic distances, and mathematical cartographyAn expanded special topics chapter, including spatial challenges with finite population modeling and spatial directional dataA thoroughly revised chapter on Bayesian inference, featuring an updated review of modern computational techniquesA dedicated GitHub repository providing R programs and solutions to selected exercises, ensuring continued access to evolving software developmentsWith refreshed content throughout, this edition serves as an essential reference for statisticians, data scientists, and researchers working with spatial data. Graduate students and professionals seeking a deep understanding of Bayesian spatial modeling will find this volume an invaluable resource for both theory and practice. The 3rd edition of this popular and authoritative text on Bayesian modeling and inference for spatial and spatial-temporal data presents a comprehensive presents a comprehensive and up-to-date treatment of hierarchical and multilevel modeling for spatial and spatio-temporal data within a Bayesian framework. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Buch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The 3rd edition of this popular and authoritative text on Bayesian modeling and inference for spatial and spatial-temporal data presents a comprehensive presents a comprehensive and up-to-date treatment of hierarchical and multilevel modeling for spatial and spatio-temporal data within a Bayesian framework.