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商品詳細
Modeling Spatio-Temporal Data : Markov Random Fields, Objective Bayes, and Multiscale Models.
・ISBN 978-1-032-62209-5 hard GB£ 145.99
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電子版あり 大学・学術機関向け電子ブック（eBook）ISBN 978-1-032-62344-3
| 著者・編者 | Ferreira, Marco A. R. (ed.), |
|---|---|
| 出版社 | (Chapman & Hall / CRC, US) |
| 出版年月 | 2024.11 |
| ページ数 | 276 pp. |
| 言語 | ENG |
| ニュース番号 | ＜731-134＞ |
解説
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.