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

Several important topics in spatial and spatio-temporal statistics developed in the last 15 years have not received enough attention in textbooks. This book aims to fill some of 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 discussed in this book include:

- Proper Gaussian Markov random fields and their use as building blocks for spatio-temporal models and multiscale models.

- Hierarchical models with intrinsic conditional autoregressive priors for spatial random effects, including reference prior, results on fast computations, and objective B