Bayesian Modeling of Spatio-Temporal Data with R (Chapman & Hall/CRC Interdisciplinary Statistics) by Sujit Sahu
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Bayesian Modeling of Spatio-Temporal Data with R (Chapman & Hall/CRC Interdisciplinary Statistics) by Sujit Sahu

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434
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About this ebook
Bayesian Modeling of Spatio-Temporal Data with R" by Sujit Sahu (published in the Chapman & Hall/CRC Interdisciplinary Statistics series) spans 12 chapters and two appendices. It covers foundational concepts, advanced theory, computations, and practical applications for analyzing space-time datasets. [1, 2]
The textbook is structured to cover the following primary topics and core themes:
1. Foundational Concepts & Exploratory Analysis
  • Spatio-Temporal Data Foundations: Introduction to the core characteristics of data collected across space and time, relevant to fields like climate science, ecology, and public health. [1, 2]
  • Stochastic Processes & Statistical Densities: The mathematical foundations necessary for characterizing probability models over spatial and temporal domains. [1]
  • Exploratory Data Analysis (EDA): Applied methods for visualizing, summarizing, and detecting patterns or dependencies in raw spatio-temporal datasets before building formal models. [1]
  • Spatial Statistics "Jargon Buster": A dedicated reference section designed to build a working vocabulary of spatial terminology without getting bogged down in high-level technicalities. [1, 2]
2. Bayesian Theory & Computation
  • Bayesian Inferential Techniques: Core theoretical frameworks for updating probabilities, defining priors, and estimating model parameters using posterior distributions.
  • Bayesian Computations: In-depth coverage of computational algorithms (such as Markov Chain Monte Carlo or MCMC) required to estimate complex spatial random-effects models.
  • Software Platforms & Packages: Instructions on utilizing industry-standard packages, including rstan, INLA, spBayes, spTimer, spTDyn, CARBayes, and CARBayesST. [1, 2, 3, 4]
3. Advanced Modeling Frameworks
  • Point-Referenced Spatio-Temporal Data: Advanced modeling strategies for data collected at exact geographic coordinates (e.g., specific weather stations or pollution monitors).
  • Areal Unit Data Modeling: Frameworks for data aggregated over defined geographic boundaries or regions (e.g., census tracts, zip codes, or counties).
  • Gaussian Processes: Comprehensive implementation details on Gaussian processes for continuous spatial surface estimation and interpolation. [1, 2, 3]
4. Applied Case Studies & R Integration
  • Dedicated Application Chapters: Practical, real-world execution chapters demonstrating how to apply these workflows to challenging datasets, including ocean chlorophyll tracking and COVID-19 health data analysis.
  • The bmstdr Package: Full integration with the author's companion R package (bmstdr), which acts as a unified vignette wrapper to simplify model fitting, cross-validation, and spatial predictions.
  • Reproducible Code: Detailed R algorithmic notes mapped to the book's tables and figures, backed by an online supplement featuring complete reproducible data and scripts. [1, 2, 3, 4]

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Topics

Advanced Modeling Frameworks Applied Case Studies & R Integration Areal Unit Data Modeling Bayesian Computations: algorithms (Markov Chain Monte Carlo or MCMC) Bayesian Inferential Techniques Bayesian Theory & Computation COVID-19 health data analysis Exploratory Data Analysis (EDA) Gaussian Processes: continuous spatial surface estimation and interpolation ocean chlorophyll tracking Point-Referenced Spatio-Temporal Data Reproducible Code: R algorithmic reproducible data and scripts Software Platforms & Packages rstan INLA spatial modeling Spatial Statistics "Jargon Buster" spatio-temporal data Spatio-Temporal Data Foundations spBayes spTimer spTDyn CARBayes and CARBayesST Stochastic Processes & Statistical Densities temporal modeling The bmstdr Package: R package (bmstdr)
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