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Modeling Spatio-Temporal Data: Markov Random Fields, Objective Bayes, and Multiscale Models

Modeling Spatio-Temporal Data: Markov Random Fields, Objective Bayes, and Multiscale Models

          
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About the Book

Several important topics in spatial and spatio-temporal statistics developed in the last 15 years have not received enough attention in textbooks. Modeling Spatio-Temporal Data: Markov Random Fields, Objectives Bayes, and Multiscale Models aims to fill this gap by providing an overview of a variety of recently proposed approaches for the analysis of spatial and spatio-temporal datasets, including proper Gaussian Markov random fields, dynamic multiscale spatio-temporal models, and objective priors for spatial and spatio-temporal models. The goal is to make these approaches more accessible to practitioners, and to stimulate additional research in these important areas of spatial and spatio-temporal statistics.

Key topics:

  • Proper Gaussian Markov random fields and their uses as building blocks for spatio-temporal models and multiscale models.
  • Hierarchical models with intrinsic conditional autoregressive priors for spatial random effects, including reference priors, results on fast computations, and objective Bayes model selection.
  • Objective priors for state-space models and a new approximate reference prior for a spatio-temporal model with dynamic spatio-temporal random effects.
  • Spatio-temporal models based on proper Gaussian Markov random fields for Poisson observations.
  • Dynamic multiscale spatio-temporal thresholding for spatial clustering and data compression.
  • Multiscale spatio-temporal assimilation of computer model output and monitoring station data.
  • Dynamic multiscale heteroscedastic multivariate spatio-temporal models.
  • The M-open multiple optima paradox and some of its practical implications for multiscale modeling.
  • Ensembles of dynamic multiscale spatio-temporal models for smooth spatio-temporal processes.

The audience for this book are practitioners, researchers, and graduate students in statistics, data science, machine learning, and related fields. Prerequisites for this book are master's-level courses on statistical inference, linear models, and Bayesian statistics. This book can be used as a textbook for a special topics course on spatial and spatio-temporal statistics, as well as supplementary material for graduate courses on spatial and spatio-temporal modeling.


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Product Details
  • ISBN-13: 9781032622095
  • Publisher: Taylor & Francis Ltd
  • Binding: Hardback
  • Language: English
  • Returnable: Y
  • Sub Title: Markov Random Fields, Objective Bayes, and Multiscale Models
  • Width: 156 mm
  • ISBN-10: 1032622091
  • Publisher Date: 04 Dec 2024
  • Height: 234 mm
  • No of Pages: 276
  • Spine Width: 18 mm
  • Weight: 589 gr


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