Buch, Englisch, 292 Seiten, Format (B × H): 161 mm x 240 mm, Gewicht: 608 g
Markov Random Fields, Objective Bayes, and Multiscale Models
Buch, Englisch, 292 Seiten, Format (B × H): 161 mm x 240 mm, Gewicht: 608 g
ISBN: 978-1-032-62209-5
Verlag: Chapman and Hall/CRC
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.
Zielgruppe
Academic and Postgraduate
Autoren/Hrsg.
Weitere Infos & Material
1. Proper Gaussian Markov Random Fields. 2. Gaussian Spatial Hierarchical Models with ICAR Priors. 3. Objective Priors for Spatio-Temporal Models. 4. Spatio-Temporal Models for Poisson Areal Data. 5. Dynamic Multiscale Spatio-Temporal Thresholding. 6. Multiscale Spatio-Temporal Data Assimilation. 7. Multiscale Heteroscedastic Multivariate Spatio-Temporal Models. 8. A Model Selection Paradox with Implications to Multiscale Modeling. 9. Ensembles of Dynamic Multiscale Spatio-Temporal Models.
