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Details for:
Ferreira M. Modeling Spatio-Temporal Data. Markov Random Fields,...2024
ferreira m modeling spatio temporal data markov random fields 2024
Type:
E-books
Files:
1
Size:
20.3 MB
Uploaded On:
Oct. 23, 2024, 12:49 p.m.
Added By:
andryold1
Seeders:
3
Leechers:
4
Info Hash:
3C525C2C3CA33005EE676D542A041555271D73DB
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Textbook in PDF format 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. Dedication Preface Editor Contributors Proper Gaussian Markov Random Fields Gaussian Spatial Hierarchical Models with ICAR Priors Objective Priors for Spatio-Temporal Models Spatio-Temporal Models for Poisson Areal Data Dynamic Multiscale Spatio-Temporal Thresholding Multiscale Spatio-Temporal Data Assimilation Multiscale Heteroscedastic Multivariate Spatio-Temporal Models A Model Selection Paradox with Implications to Multiscale Modeling Ensembles of Dynamic Multiscale Spatio-Temporal Models Bibliography Author Index Subject Index
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Ferreira M. Modeling Spatio-Temporal Data. Markov Random Fields,...2024.pdf
20.3 MB
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