{"product_id":"modeling-spatio-temporal-data","title":"Modeling Spatio-Temporal Data: Markov Random Fields, Objective Bayes, and Multiscale Models","description":"\u003cp\u003eSeveral important topics in spatial and spatio-temporal statistics developed in the last 15 years have not received enough attention in textbooks.\u003cstrong\u003eModeling Spatio-Temporal Data: Markov Random Fields, Objectives Bayes, and Multiscale Models\u003c\/strong\u003eaims 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.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eKey topics:\u003c\/strong\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eProper Gaussian Markov random fields and their uses as building blocks for spatio-temporal models and multiscale models.\u003c\/li\u003e\n\u003cli\u003eHierarchical models with intrinsic conditional autoregressive priors for spatial random effects, including reference priors, results on fast computations, and objective Bayes model selection.\u003c\/li\u003e\n\u003cli\u003eObjective priors for state-space models and a new approximate reference prior for a spatio-temporal model with dynamic spatio-temporal random effects.\u003c\/li\u003e\n\u003cli\u003eSpatio-temporal models based on proper Gaussian Markov random fields for Poisson observations.\u003c\/li\u003e\n\u003cli\u003eDynamic multiscale spatio-temporal thresholding for spatial clustering and data compression.\u003c\/li\u003e\n\u003cli\u003eMultiscale spatio-temporal assimilation of computer model output and monitoring station data.\u003c\/li\u003e\n\u003cli\u003eDynamic multiscale heteroscedastic multivariate spatio-temporal models.\u003c\/li\u003e\n\u003cli\u003eThe M-open multiple optima paradox and some of its practical implications for multiscale modeling.\u003c\/li\u003e\n\u003cli\u003eEnsembles of dynamic multiscale spatio-temporal models for smooth spatio-temporal processes.\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003eThe 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.\u003c\/p\u003e","brand":"None","offers":[{"title":"Hardcover","offer_id":46240981090514,"sku":"9781032622095","price":281.5,"currency_code":"CAD","in_stock":true},{"title":"Kobo eBook","offer_id":46240981123282,"sku":"4acd934f-cf02-30c9-a846-e307ee32a784","price":107.17,"currency_code":"CAD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0655\/8980\/5233\/files\/1_c9f0d9fe-f37a-4cbf-b8ca-0a45767f03d6.jpg?v=1763172894","url":"https:\/\/iandigo.com\/products\/modeling-spatio-temporal-data","provider":"Indigo","version":"1.0","type":"link"}