Hierarchical Modeling and Analysis for Spatial Data
暫譯: 空間數據的階層建模與分析
Banerjee, Sudipto, Gelfand, Alan E., Carlin, Bradley P.
相關主題
商品描述
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 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.
商品描述(中文翻譯)
《空間數據的階層建模與分析（第三版）》是這本受歡迎且權威的書籍最新版本，專注於空間及時空數據的貝葉斯建模與推斷。該書在貝葉斯框架下，全面且最新地介紹了空間及時空數據的階層和多層次建模。自第二版以來的十年間，空間統計學因數據可用性的激增和貝葉斯計算的進步而顯著發展。本版反映了這些變化，介紹了新方法、擴展的應用以及增強的計算資源，以支持環境科學、生態學和公共衛生等各學科的研究人員和實務工作者。
第三版的主要特點：
- 專門章節介紹大型空間和時空數據集的最先進貝葉斯建模
- 兩個新章節關於空間點模式分析，涵蓋基礎和貝葉斯觀點
- 新增章節介紹空間數據融合，整合來自不同概率機制的多樣空間數據來源
- 易於理解的GPS映射、測地距離和數學製圖入門
- 擴展的專題章節，包括有限人口建模的空間挑戰和空間方向數據
- 徹底修訂的貝葉斯推斷章節，包含對現代計算技術的更新回顧
- 專門的GitHub資源庫提供R程式和選定練習的解答，確保持續訪問不斷發展的軟體開發
本版內容經過更新，成為統計學家、數據科學家和從事空間數據研究的研究人員的重要參考資料。研究生和專業人士若希望深入理解貝葉斯空間建模，將會發現這本書對於理論和實踐都是無價的資源。
作者簡介
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 books, Gelfand is internationally known for his contributions to applied statistics, Bayesian computation and Bayesian inference. For the past thirty years, Gelfand's primary research focus has been in the area of statistical modeling for spatial and space-time data. He has advanced methodology, using the Bayesian paradigm, to associate fully model-based inference with spatial and space-time data. His chief areas of application include spatio-temporal environmental and ecological processes.
Sudipto Banerjee is Professor of Biostatistics and Senior Associate Dean for Academic Programs in the Fielding School of Public Health at the University of California, Los Angeles (UCLA). He holds joint appointments as a Professor in the UCLA Department of Statistics and Data Science and as an Affiliate faculty in the UCLA Institute of Environment and Sustainability. Banerjee has authored over 200 research articles, 2 textbooks, 2 committee reports for the National Research Council of the National Academies, and an edited handbook on spatial epidemiology. Banerjee is well-known for his research expertise and methodological advancements in Bayesian hierarchical modeling and inference for spatial-temporal data; theoretical and computational developments for Gaussian processes; environmental processes and their impact on public health; spatial epidemiology; stochastic process models; statistical learning from physical and mechanistic systems; survey sampling and survival analysis.
作者簡介(中文翻譯)
艾倫·E·吉爾芬德是杜克大學統計科學的詹姆斯·B·杜克名譽教授。他還在尼古拉斯學院擔任環境科學與政策的教授。吉爾芬德是超過330篇論文和6本書的作者，因其在應用統計、貝葉斯計算和貝葉斯推斷方面的貢獻而享有國際聲譽。在過去的三十年中，吉爾芬德的主要研究重點是針對空間和時空數據的統計建模。他利用貝葉斯範式推進了方法論，將完全基於模型的推斷與空間和時空數據相結合。他的主要應用領域包括時空環境和生態過程。
蘇迪普托·巴納吉是加州大學洛杉磯分校（UCLA）公共衛生學院生物統計學教授及學術項目的高級副院長。他在UCLA統計與數據科學系擔任教授，並在UCLA環境與可持續性研究所擔任附屬教員。巴納吉已發表超過200篇研究文章，撰寫了2本教科書、2份國家科學院國家研究委員會的委員會報告，以及一本關於空間流行病學的編輯手冊。巴納吉以其在貝葉斯層級建模和時空數據推斷方面的研究專長和方法學進展而聞名；在高斯過程的理論和計算發展；環境過程及其對公共衛生的影響；空間流行病學；隨機過程模型；從物理和機械系統中進行統計學習；調查取樣和生存分析等領域。
