{"product_id":"using-r-for-bayesian-spatial-and-spatio-temporal-health-modeling-9780367490126","title":"Using R for Bayesian Spatial and Spatio-Temporal Health Modeling","description":"\u003cp\u003e • Author(s): Andrew B. Lawson\u003cbr\u003e • Publisher: Taylor \u0026amp; Francis\u003cbr\u003e • Publisher Imprint: CRC Press\u003cbr\u003e • BISAC: Probability \u0026amp; Statistics - General\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eProgressively more and more attention has been paid to how location affects health outcomes. The area of \u003ci\u003edisease mapping\u003c\/i\u003e focusses on these problems, and the Bayesian paradigm has a major role to play in the understanding of the complex interplay of context and individual predisposition in such studies of disease. \u003cb\u003e\u003ci\u003eUsing R for Bayesian Spatial and Spatio-Temporal Health Modeling\u003c\/i\u003e\u003c\/b\u003e provides a major resource for those interested in applying Bayesian methodology in small area health data studies.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eFeatures: \u003c\/b\u003e\u003c\/p\u003e\u003cul\u003e \u003cli\u003eReview of R graphics relevant to spatial health data\u003c\/li\u003e \u003c\/ul\u003e\u003cul\u003e \u003cli\u003eOverview of Bayesian methods and Bayesian hierarchical modeling as applied to spatial data\u003c\/li\u003e \u003c\/ul\u003e\u003cul\u003e \u003cli\u003eBayesian Computation and goodness-of-fit\u003c\/li\u003e \u003c\/ul\u003e\u003cul\u003e \u003cli\u003eReview of basic Bayesian disease mapping models\u003c\/li\u003e \u003c\/ul\u003e\u003cul\u003e \u003cli\u003eSpatio-temporal modeling with MCMC and INLA\u003c\/li\u003e \u003c\/ul\u003e\u003cul\u003e \u003cli\u003eSpecial topics include multivariate models, survival analysis, missing data, measurement error, variable selection, individual event modeling, and infectious disease modeling\u003c\/li\u003e \u003c\/ul\u003e\u003cul\u003e \u003cli\u003eSoftware for fitting models based on BRugs, Nimble, CARBayes and INLA\u003c\/li\u003e \u003c\/ul\u003e\u003cul\u003e \u003cli\u003eProvides code relevant to fitting all examples throughout the book at a supplementary website \u003c\/li\u003e \u003c\/ul\u003e\u003cp\u003eThe book fills a void in the literature and available software, providing a crucial link for students and professionals alike to engage in the analysis of spatial and spatio-temporal health data from a Bayesian perspective using R. The book emphasizes the use of MCMC via Nimble, BRugs, and CARBAyes, but also includes INLA for comparative purposes. In addition, a wide range of packages useful in the analysis of geo-referenced spatial data are employed and code is provided. It will likely become a key reference for researchers and students from biostatistics, epidemiology, public health, and environmental science.\u003c\/p\u003e","brand":"Taylor \u0026 Francis","offers":[{"title":"Hardcover","offer_id":45241038340247,"sku":"9780367490126","price":11730.0,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0666\/3471\/1191\/files\/9780367490126.webp?v=1769227351","url":"https:\/\/atlanticbooks.com\/products\/using-r-for-bayesian-spatial-and-spatio-temporal-health-modeling-9780367490126","provider":"Atlantic Books","version":"1.0","type":"link"}