Hierarchical Modeling and Analysis for Spatial Data, Third Edition is the 3rd edition of this popular and authoritative text on Bayesian modeling and inference for spatial and spatial-temporal data. The text 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. 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 datasets
Two new chapters on spatial point pattern analysis, covering both foundational and Bayesian perspectives
A new chapter on spatial data fusion, integrating diverse spatial data sources from different probabilistic mechanisms
An accessible introduction to GPS mapping, geodesic distances, and mathematical cartography
An expanded special topics chapter, including spatial challenges with finite population modeling and spatial directional data
A thoroughly revised chapter on Bayesian inference, featuring an updated review of modern computational techniques
A dedicated GitHub repository providing R programs and solutions to selected exercises, ensuring continued access to evolving software developments

With 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.

Cr�ticas:

"This book was a pleasure to review. Most of the emphasis is on insight and intuition with relatively little on traditional multivariate techniques. I also found some of the explanations delightful[W]hile they did not convert me to Bayesianism, [the authors] made me reconsider some of my assumptions. They later state 'Our book is intended as a research monograph, presenting the state of the art' and my impression is that they have succeededIn many sections the formulae are augmented by showing R or S code, making it easy to actually apply the mathematics. In summary, this is a nice book." -Short Book Reviews of the International Statistical Institute "The book contains a wealth of material not available elsewhere in a unified manner. Each chapter contains worked out examples using some well known software packages and has exercises with related computer code and data on a supporting web page. The book is up to date in its coveragean important addition to the literature on spatial data analysis." -Zentralblatt MATH 1053

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