Using R for Bayesian Spatial and Spatio-Temporal Health Modeling - Info and Reading Options
By Andrew B. Lawson
"Using R for Bayesian Spatial and Spatio-Temporal Health Modeling" was published by Taylor & Francis Group in 2021 - Abingdon, Oxon ; Boca Raton, it has 284 pages and the language of the book is English.
“Using R for Bayesian Spatial and Spatio-Temporal Health Modeling” Metadata:
- Title: ➤ Using R for Bayesian Spatial and Spatio-Temporal Health Modeling
- Author: Andrew B. Lawson
- Language: English
- Number of Pages: 284
- Publisher: Taylor & Francis Group
- Publish Date: 2021
- Publish Location: Abingdon, Oxon ; Boca Raton
“Using R for Bayesian Spatial and Spatio-Temporal Health Modeling” Subjects and Themes:
- Subjects: ➤ Medical statistics - Data processing - Medical mapping - Computer programs - Geospatial data - Computer processing - Geographic information systems - Information modeling - Simulation methods - R (Computer program language) - Bayesian statistical decision theory - Geographic Information Systems - Cartographie médicale - Informatique - Données géospatiales - Systèmes d'information géographique - Modèles d'information - Méthodes de simulation - R (Langage de programmation) - Théorie de la décision bayésienne - MATHEMATICS / Probability & Statistics / General - MEDICAL / Epidemiology
Edition Identifiers:
- The Open Library ID: OL33836904M - OL25272744W
- Online Computer Library Center (OCLC) ID: 1224584843 - 1249750321
- Library of Congress Control Number (LCCN): 2020049395
- ISBN-13: 9781003043997
- All ISBNs: 9781003043997
AI-generated Review of “Using R for Bayesian Spatial and Spatio-Temporal Health Modeling”:
"Using R for Bayesian Spatial and Spatio-Temporal Health Modeling" Description:
Open Data:
"The 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"--
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